Student in Computational Optimization at Huawei R&D Sites in Belgium and the Netherlands

Back

Data Annotation Intern

Amsterdam-Zuidoost, Netherlands

Employee

No remote work

Huawei Amsterdam Research Center

For our R&D Centre in Amsterdam we are looking for a Data Annotation Intern to join our team

About The Job

You'll work as an integral part of an operational team, sifting language materials against requirements to identify reportable intelligence that will be forwarded to analysts to help shape and influence their operational decision making.

As a Data Annotation Intern, you will be involved into evaluating the performance of the system, annotating linguistic data and working closely with our engineering team, including managing the language data systems for NLP development. You will use your analytical mindset, strong communication skills, collaborative attitude, and personal drive to increase the performance of the system.

Responsibilities

Provide feedback for existing tools that can improve the accuracy and quality of deep learning models
Ability to work on repetitive tasks efficiently and effectively with minimal errors
Comfortable to handle large data sets on a regular basis
Strong interest in Artificial Intelligence and machine learning, especially in deep learning

Qualifications

Students major in languages, linguistics, translation or similar
1+ years of experience in Analytical, Logical, Eye for detail and self-learning with minimal support
Native in Arabic, Malay or Thai & deep appreciation of local nuances
Good web research skills and analytical abilities
Comprehension and written communication skills in English (more language skills are a plus)
Broad range of interests, with specific areas of expertise are a plus

Back

Apply

Back

Jr PhD Technical Engineer

Leuven, Belgium

Employee

Partial Remote Job

Huawei Leuven Research Center

Huawei Gent Research Center

Location: China

Salary: Super competitive compensation package for Top Talents (3 to 5 times of Belgium market pay)

Job Overview:

We are seeking a top talent Junior Technical Engineer to work on challenging global projects in various majors including AI, Mathematics, Computer Science, Software Engineering, Physical/Materials and so on. This position is suitable for recent PhD graduates who have achieved exceptional grades, won top competitions such as High School Mathematics/Physics, ICPC, IMC, or have been granted patent applications and published papers in top magazines. The successful candidate will receive a world-class mentorship from our top experts and fellows.

Job Responsibilities:

- Develop cutting-edge technologies for various challenging global projects ranging from artificial intelligence framework, robotic technology, computer vision, and more

- Take part in research initiatives and propose innovative approaches to solving complex technical problems

- Work with a team of highly talented engineers, designers, and architects who are passionate about innovation and fostering creativity

- Collaborate with cross-functional teams to ensure efficient project delivery and maintenance

- Constantly learn and stay updated with the latest technology trends.

Required Skills and Qualifications:

- M.Sc. or Ph.D. degree in majors such as AI, Mathematics, Computer Science, Software Engineering, Physical/Materials, etc.

- Strong analytical, problem-solving and communication skills

- Excellent programming skills with proficiency in one or more programming languages such as Python, C++, etc.

- Familiarity with machine learning, deep learning, robotics, computer vision, or other related fields.

- Strong research and publication record, with an ability to think critically and propose innovative solutions.

Why Huawei?

Huawei is a leading global information and communications technology (ICT) solutions provider and a world-class employer. We believe innovation drives progress, and we invest heavily in basic research, concentrating on technological breakthroughs that drive the world forward. As a Junior Technical Engineer in Huawei's Junior Technical Engineer Plan, you will have the opportunity to work on some of the world's most challenging and exciting projects related to 37 topics encompassing advanced technologies like :

1、Key technologies of the next-generation AI framework

2、Study on the Robustness of Deep Intensification Learning

3、Study on Hierarchical Strengthening Learning for Automatic Driving Application

4、Study on the Method of Minimal Target Segmentation in Medical Imaging

5、Formal Verification Platform Based on LLVM Intermediate Code

6、All-interactive intelligent project management assistant

7、Modeling and Navigation of Autonomous Robot in Unfamiliar Environment

8、Obstacle avoidance control for indoor flying robot

9、displayed in a hover box

10、Holographic video

11、Key points of dense 3D

12、Facial kinship recognition

13、Panoramic segmentation (Panoptic Segmentation)

14、Video Semantic analysis and identification

15、Research on High Precision and High Precision NVH Simulation Technology of Electric Drive System

16、Breakthrough in vulnerability discovery

17、Secure programming language

18、Proof of crash consistency of concurrent file systems

19、Real-time video quality detection

20、In-situ intelligent (In-Situ AI)

21、Active collaborative interaction technology

22、Research on Software Stack Based on New Hardware Interconnection

23、Research on Operating System and Virtualization Technology in Data Center Open Architecture

24、Research on Deterministic Technology of Automatic Driving

25、The ultimate performance of embedded software is doubled in two years.

26、Ark compiler

27、MindSpore depth probability calculation MindSpore

28、MindSpore Causal Cognitive Calculation MindSpore

29、Study the interpretability of AI technologies.

30、Data Storage and Optimization Oriented to Efficient AI

31、Multi-mode converged search

32、Knowledge-based semantic computing

33、low latency communications (LLC)

34、Future-oriented 3D GIS-Semantic, Mesh Simplification, and Indoor High-Precision Positioning

35、NLP

36、Knowledge Graph

37、Speach Recognition

……（consistently updated ）

Back

Apply

Back

Photonics Design Expert

Gent, Belgium

Employee

Huawei Gent Research Center

Huawei Belgium Research and Development Group (formally Caliopa) is a center of excellence for silicon photonics design and processing. It was initially created as a spin-off from the University of Gent and Imec in 2010 but it has been acquired by Huawei in 2013. Huawei is the global No. 2 telecom solution provider and uses approximately one sixth of all photonics components produced in the world.

Huawei aims to significantly reduce the cost of these components by investing aggressively in silicon photonics research and development and implementing solutions based on this technology. Huawei Belgium R&D group is at the heart of this program. The group is located in Gent, a vibrant city in Belgium in the heart of Europe.

To strengthen our team, Caliopa is now looking for a Photonics Design Expert.

Job responsibilities:

Work as part of a team of photonic engineers and product designers dedicated to develop Huawei’s silicon photonics platform
Lead new projects from R&D stage toward new products introduction (NPI)
Contribute to the design and validation of photonic components, subsystems and circuits
Coordinate test plans and analyze experimental data in the R&D phase
Work closely with component/module teams
Work closely with the processing and test teams
Depending on your skills and interests, the job can focus more on next-generation product development or developing cutting edge technologies for application further in the future

Requirements & Qualifications:

In depth knowledge of integrated optics and optoelectronics components and systems
Education requirements:
- D. in Photonics, Electrical Engineering, Physics, or related field, 5+ years of work experience in a photonics industry (R&D or production department)
Experience requirements:
- Design and test of silicon photonics and III-V components
- Working knowledge of how to integrate PICs with EICs, RF ICs, and driving circuitry in transceiver modules or equivalent sub-system applications
- Proven track record of project/product management skills, bringing new components or systems to market in volume
- Experience in product development (spec. definition, customer-oriented attitude, business acumen for new business opportunity) would be an asset
- Hands on experience with photonics modelling tools (e.g. Photon design, Lumerical, COMSOL, CST)
- Hands on experience with layout tools (e.g. Tanner L-Edit, Luceda Ipkiss).
- Experience with Luceda Ipkiss would be an advantage
- Experience with Python would be an advantage
Language:
- Fluent in English (written and spoken),
- Dutch or Chinese language knowledge is a plus
Attitude:
- Strong team player
- Excellent written and oral communication skills
- Creativity in problem-solving
- Customer centric
- Proactive

Back

Apply

Back

Predictive Network Modeling Research Student

Leuven, Belgium

Student

Huawei Leuven Research Center

Rough network data being typically unlabeled limits application of neural networks to unsupervised learning tasks (learning from unlabeled data). Tools for automated network data labeling becomes thus a first-level priority to enable supervised learning tasks (learning from hypothesis/candidate models) by means of neural networks. These neural networks are primarily those involved in nonlinear function approximation underlying automated pattern detection/extraction and recognition/identification such as multi-dimensional interaction modeling and their synthetic representation. In the context of communication networks, for instance, labeled datasets can be used for training neural models to augment -or even replace- current local routing and forwarding process.

On the other hand, a label can be considered as encoding the value of the objective function underlying the machine learning task or the parameter of a model (structured learning). Hence, automated labeling implies that either this objective or the model are formally known. This operation can be realized either in batch or online mode. The former typically assumes that the main performance criteria relates to the size of the input (assumed to be complete) that needs to be processed to reach a given accuracy level. The latter implies time-constrained processing of incoming input following different arrival processes; thus, the number of cycles/inter-arrival times required to reach a given accuracy level.

Objective: automatic labeling method of network data, including spatio-temporal data (traffic, etc.), structural data (topological, etc.)

Task: blends modeling, algorithmic and experimental research; the candidate will have the opportunity to explore, evaluate and compare different labeling algorithms by formal and numerical methods. These tasks will be realized under supervision of senior (postdoc-level) researcher.

Candidate profile:

if MSc thesis: the candidate must be following the last year of the curriculum in, e.g., Applied mathematics, Math engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
if internship: the candidate must have completed his MSc (in one of these disciplines). Copy of the MSc diploma/certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
Good knowledge of traffic/network simulation is considered as a plus.

Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Back

Apply

Back

Predictive Network Modeling Research Student

Leuven, Belgium

Student

Huawei Leuven Research Center

Rough network data being typically unlabeled limits application of neural networks to unsupervised learning tasks (learning from unlabeled data). Tools for automated network data labeling becomes thus a first-level priority to enable supervised learning tasks (learning from hypothesis/candidate models) by means of neural networks. These neural networks are primarily those involved in nonlinear function approximation underlying automated pattern detection/extraction and recognition/identification such as multi-dimensional interaction modeling and their synthetic representation. In the context of communication networks, for instance, labeled datasets can be used for training neural models to augment -or even replace- current local routing and forwarding process.

On the other hand, a label can be considered as encoding the value of the objective function underlying the machine learning task or the parameter of a model (structured learning). Hence, automated labeling implies that either this objective or the model are formally known. This operation can be realized either in batch or online mode. The former typically assumes that the main performance criteria relates to the size of the input (assumed to be complete) that needs to be processed to reach a given accuracy level. The latter implies time-constrained processing of incoming input following different arrival processes; thus, the number of cycles/inter-arrival times required to reach a given accuracy level.

Objective: automatic labeling method of network data, including spatio-temporal data (traffic, etc.), structural data (topological, etc.)

Task: blends modeling, algorithmic and experimental research; the candidate will have the opportunity to explore, evaluate and compare different labeling algorithms by formal and numerical methods. These tasks will be realized under supervision of senior (postdoc-level) researcher.

Duration: from 6 months to 1 year (max.). Possibility to combine with MSc Thesis.

Candidate profile:

if MSc thesis: the candidate must be following the last year of the curriculum in, e.g., Applied mathematics, Math engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
if internship: the candidate must have completed his MSc (in one of these disciplines). Copy of the MSc diploma/certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
Good knowledge of traffic/network simulation is considered as a plus.

Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Back

Apply

Back

R&D N&O Student

Leuven, Belgium

Internship

Partial Remote Job

Huawei Leuven Research Center

Huawei Gent Research Center

Our N&O Lab @ Huawei Belgium Research Center is offering exciting internship opportunities for Master's students with a passion for cutting-edge research, innovation and technology.

As an intern, you will be working closely with our highly skilled research and development teams, gaining hands-on experience in various projects that address some of the most pressing challenges in technology today.

You will have the opportunity to participate in research initiatives that focus on the development of sophisticated, state-of-the-art technologies and solutions in areas such as artificial intelligence & machine learning.

You will work on challenging assignments alongside seasoned professionals, perform experiments that contribute to research projects, and learn how to analyze, interpret and communicate your results.

The ideal candidate must be pursuing a Master's degree in a field related to Computer Science, Applied Mathematics, or other related technical disciplines.

Candidates should possess a strong mathematical background, top-notch algorithmic and analytical/problem-solving skills, as well as the ability to work independently in a fast-paced, dynamic environment.

Excellent programming skills in one or more programming languages such as C/C++, Fortran or Julia are also required.

This internship provides an excellent opportunity to consolidate fundamentals and gain practical experience in a fast-paced technology environment while contributing to ongoing cutting-edge research initiatives.

We offer a supportive, collaborative workplace culture with plenty of opportunities to learn and grow.

If you are interested in this exciting internship opportunity, we encourage you to apply.

Back

Apply

Back

Spontaneous Application

Leuven, Belgium

Employee

Huawei Leuven Research Center

Huawei Gent Research Center

Huawei Amsterdam Research Center

Spontaneous Application

Back

Apply

Back

Student in Composite Optimization

Leuven, Belgium

Student

Option for/Allows Remote Job

Huawei Leuven Research Center

Context: Optimization problems in the context of distributed computing, communication networks, etc often involve multiple terms in the objective function. Minimizing the sum of a smooth (continuously differentiable) and a lower semi-continuous function can be solved by means of proximal gradient methods. The nonmonotone variant applies when the derivative of the smooth part of the objective function is not globally but only locally Lipschitz continuous. In this case, the method is referred to as nonmonotone proximal gradient. The properties of the latter remain open for investigation when inertial terms are considered and for constrained problems.

Goal: the goal of this internship is to design iterative algorithm and demonstrate their local convergence properties. The internship can be completed by numerical experiments and comparative performance analysis.

Internship Position requirements

MSc degree (last year) or ongoing PhD degree in Applied mathematics, Mathematical engineering, Computational Mathematics/Algorithmic or Computer science (engineering).

Note well: candidate must have obtained their University degree from an academic institution of one of the EU countries.

Excellent-level

Continuous optimization including iterative and proximal/projective methods
Nonsmooth optimization including subdifferential and nonsmooth/variational analysis

Good-level in numerical programming languages (Octave)
Entry-level in programming language (C/C++, FORTRAN) is considered as a strong plus.
Excellent communication skills : presentation, oral and writing.

Seniority Level: Entry level

Back

Apply

Back

Student in Composite Optimization

Leuven, Belgium

Internship

Option for/Allows Remote Job

Huawei Leuven Research Center

Context: Optimization problems in the context of distributed computing, communication networks, etc often involve multiple terms in the objective function. Minimizing the sum of a smooth (continuously differentiable) and a lower semi-continuous function can be solved by means of proximal gradient methods. The nonmonotone variant applies when the derivative of the smooth part of the objective function is not globally but only locally Lipschitz continuous. In this case, the method is referred to as nonmonotone proximal gradient. The properties of the latter remain open for investigation when inertial terms are considered as well as for (non)linearly constrained problems.

Goal: the goal of this internship is to design iterative algorithms and demonstrate their local convergence properties. The internship can be completed by numerical experiments and comparative performance analysis.

Internship Position requirements

MSc degree (last year) or ongoing PhD degree in Applied mathematics, Mathematical engineering, Computational Mathematics/Algorithmic or Computer science (engineering).

Note well: candidate must have obtained their University degree from an academic institution of one of the EU countries.

Excellent-level

Continuous optimization including iterative and proximal/projective methods
Nonsmooth optimization including subdifferential and nonsmooth/variational analysis

Good-level in numerical programming languages (Octave)
Entry-level in programming language (C/C++, FORTRAN) is considered as a strong plus.
Excellent communication skills : presentation, oral and writing.

Seniority Level: Entry level

Duration:

Fixed term: up to 1 years, possibility to combine with MSc Thesis.
Full-time (preferred) or part-time.

Starting date: March 1st, 2023 (earliest) - Sep.30, 2023 (latest)

Back

Apply

Back

Student in Computational Optimization

Leuven, Belgium

Internship

Option for/Allows Remote Job

Huawei Leuven Research Center

Our Research Lab - European Branch Division plays a leading role in researching new technologies for next generation networks. These include a.o. the inception of fast converging algorithms to support online/sequential network decision processes. Due to various operational and performance requirements, reaching this target requires the solving of challenging mathematical optimization problems involving nonlinear coupling constraints, nonconvex composite objectives, etc.

In order to enlarge the team of high-end experts engaged in this research work, we invite applications for internship position in Computational Optimization and its aplication to communication networks including including network design, routing/traffic assignment, resource allocation, flow/resource scheduling, etc.

Tasks

Investigate emerging mathematical, algorithmic and computational methods for the online/batch solving of large-scale network optimization problems involving nonlinear coupling constraints, nonconvex composite objectives, etc.
Contribute to the experimentation of new computational optimization methods
Participate to the development of experimental tools to validate and compare them through numerical experiments

Internship Position requirements

MSc degree (last year) or ongoing PhD degree in Applied mathematics, Mathematical engineering, Computational Mathematics/Algorithmic or Computer science (engineering).
- Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.
Entry-level practical experience in either
- Continuous optimization / nonlinear programming (NLP) including iterative and projective methods
- or Mixed-integer nonlinear programming (MINLP) and related
Good knowledge of programming language (C/C++, FORTRAN). Knowledge of numerical programming languages (Octave) is considered as a strong plus.
Excellent communication skills : presentation, oral and writing.

Seniority Level:

Entry level

Duration:

Fixed term: up to 2 years (renewable), possibility to combine with MSc Thesis.
Full-time (preferred) or part-time.

Starting date: January 1st, 2023 (earliest) - Sep.30, 2023 (latest)

Back

Apply

Back

Student in Fast Converging Solvers

Leuven, Belgium

Student

Huawei Leuven Research Center

Despite recent advances in automatic differentiation (AD) algorithms and software, incorporating automatic differentiation tools in optimization solvers remains largely unexploited. Commonly, the solving of bound-constrained nonlinear optimization problems with, e.g., the Newton method or the Augmented Lagrangian Method (ALM), require the evaluation of the objective function, its gradient and the (sparsity pattern of the) Hessian matrix. Additionally, constrained optimization problems also involve providing the sparsity pattern and the Jacobian matrix of the constraints. The time and resource required to obtain this information and verify their correctness can be relatively large even for simple problems. Nowadays, optimization problem-solving environments provide modeling language and state-of-the-art optimization solvers together with packages that are capable to compute first-order information, e.g., derivatives, gradients. However, AD remains largely unexploited for generating these quantities; requiring consequently to run the entire framework and its packages altogether to solve nonlinear optimization problems. In this context, the main goal is to blend AD tools that automatically generate the required first- and second-order quantities together with nonlinear optimization methods such as ALM for constrained problems.

Objective: instigate and investigate automatic and numerical differentiation tools for nonconvex optimization solvers that can rapidly converge with few function and derivative evaluations as well as provide and improve second-order information with the same efficiency and reliability as available for first-order information.

Task(s): design/formulate, develop and numerically evaluate enhancements of existing computational methods/algorithms such as ALM for solving constrained nonconvex optimization problems. To address the computational performance objectives, the candidate will also actively participate to the design (extension) and evaluation of (existing) automatic differentiation tools for solving such optimization problems. These tasks will be realized under the supervision of a senior (postdoc-level) researcher.

Candidate profile:

If MSc thesis : the candidate must be following the last year of the curriculum in, e.g., Applied mathematics, Math. engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
If PhD student : the candidate must have completed his/her MSc (in one of these disciplines) and ideally realized a thesis in one of the following domain(s): nonlinear programming, computational methods and algorithms for nonconvex optimization (incl. second order iterative methods, subspace methods, etc.), automatic differentiation. Copy of the MSc diploma/ certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
Good knowledge of functional programming language (LISP, Julia, etc.) is considered as a plus.

Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Back

Apply

Back

Student in Fast Converging Solvers

Leuven, Belgium

Internship

Huawei Leuven Research Center

Despite recent advances in automatic differentiation (AD) algorithms and software, incorporating automatic differentiation tools in optimization solvers remains largely unexploited. Commonly, the solving of bound-constrained nonlinear optimization problems with, e.g., the Newton method or the Augmented Lagrangian Method (ALM), require the evaluation of the objective function, its gradient and the (sparsity pattern of the) Hessian matrix. Additionally, constrained optimization problems also involve providing the sparsity pattern and the Jacobian matrix of the constraints. The time and resource required to obtain this information and verify their correctness can be relatively large even for simple problems. Nowadays, optimization problem-solving environments provide modeling language and state-of-the-art optimization solvers together with packages that are capable to compute first-order information, e.g., derivatives, gradients. However, AD remains largely unexploited for generating these quantities; requiring consequently to run the entire framework and its packages altogether to solve nonlinear optimization problems. In this context, the main goal is to blend AD tools that automatically generate the required first- and second-order quantities together with nonlinear optimization methods such as ALM for constrained problems.

Objective: instigate and investigate automatic and numerical differentiation tools for nonconvex optimization solvers that can rapidly converge with few function and derivative evaluations as well as provide and improve second-order information with the same efficiency and reliability as available for first-order information.

Task(s): design/formulate, develop and numerically evaluate enhancements of existing computational methods/algorithms such as ALM for solving constrained nonconvex optimization problems. To address the computational performance objectives, the candidate will also actively participate to the design (extension) and evaluation of (existing) automatic differentiation tools for solving such optimization problems. These tasks will be realized under the supervision of a senior (postdoc-level) researcher.

Duration: from 3 months to 1 year (max.)

Candidate profile:

If MSc thesis internship: the candidate must be following the last year of the curriculum in, e.g., Applied mathematics, Math. engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
If PhD student internship: the candidate must have completed his/her MSc (in one of these disciplines) and ideally realized a thesis in one of the following domain(s): nonlinear programming, computational methods and algorithms for nonconvex optimization (incl. second order iterative methods, subspace methods, etc.), automatic differentiation. Copy of the MSc diploma/ certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
Good knowledge of functional programming language (LISP, Julia, etc.) is considered as a plus.

Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Back

Apply

Back

Student in Network Optimization

Leuven, Belgium

Internship

Huawei Leuven Research Center

The Belgium Research Lab plays a leading role in researching novel technologies for next generation networks. These include a.o. the inception of fast converging algorithms customized to the solving of large-scale mathematical optimization problems involving nonlinear coupling constraints, nonconvex composite objectives, etc. In order to enlarge the team of high-end experts engaged in this research work, we invite for internship positions in Network Optimization fundamentals and applications, i.e., network design, routing/traffic assignment, resource allocation, flow/resource scheduling, etc.

Goals

Together with the research & engineering team in advanced mathematical and computational optimization focused on network applications, you will contribute to either of the following tasks

Develop methods and algorithms customized for network optimization problems involving nonlinear coupling constraints, nonconvex/nonlinear composite objective functions, etc.
Evaluate numerically these network optimization algorithms on selected use cases with synthetic and/or real-life data sets.
Experiement them as part of a mathematical solver framework for their automated execution

Skills and competences

Network optimization including network design, routing/traffic assignment, resource allocation, flow/resource scheduling, etc.
Optimization methods/algorithms (must) : mixed integer (non)linear programming and at least one of the following:

o Iterative methods: first order methods (linesearch methods, subspace methods, etc.), Gauss-Siedel, Jacobi, Newton-Raphson methods, etc., Primal-dual methods (Lagrangian methods, etc.), interior point methods
o Projective/proximal methods
o Combinatorial methods: polyhedral methods, branch-and-cut, etc.

Programming skills (must) : Octave and either C/C++ or FORTRAN (knowledge of Julia programming is considered as a plus)
Knowledge of solving frameworks : (MI)NLP (e.g. Gurobi, SCIP, Couenne/COIN-OR, SHOT/COIN-OR, Algencan, Bonmin, etc.), Subsolvers (e.g. MIP: CPlex, NLP: Ipopt, Conopt) is considered as a strong plus

Duration

Fixed term: from 3 months up to 2 years (renewable), possibility to combine with MSc Thesis.
Full-time (preferred) or part-time.

Starting date: January 1st, 2023 (earliest) - Sep.30, 2023 (latest)

Candidate profile

if MSc thesis: the candidate must be following the last year of the curriculum in, e.g., Applied/ Numerical mathematics, Math/Mechanical engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
if studentship/internship: the candidate must either be last year of an MSc curriculum in one of these disciplines or have completed an MSc (in one of these disciplines). Copy of the MSc diploma/certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Application : please provide complete CV with both curriculum and practical experience, including elements that demonstrate

Good programming skills (experimental code) in Octave and either C/C++ or Fortran
Knowledge in the handling of optimization methods and algorithms
Excellent English level proficiency (both oral and written)

Contact person:

Nicolas Spindelböck

HR Consultant

Back

Apply

Back

Student in Nonlinear Inverse Problems

Leuven, Belgium

Student

Huawei Leuven Research Center

Inverse problems seek to reconstruct the unknown model parameters characterizing the system under investigation from measured data. More precisely, nonlinear inverse problems start by considering a nonlinear operator between the parameters, and acquired information / observation about them, i.e., data or measurements. This operator constructs a model for the available data, assumed to depend only on these parameters. Since the inverse problem is the inverse of the forward problem, this operator is often referred to as the forward operator. n nonlinear inverse problems, the forward operator being nonlinear, the model parameters depend in a nonlinear way of the state of the system and therefore on the observations available on it.

However, inverse problems are typically ill-posed, as opposed to the well-posed problems (verifying existence, uniqueness, and stability of the solution(s)). Also inverse problems are also typically ill-conditioned, i.e., a small change in the input induce a large change in the response. Hence, to tackles them, nonlinear regularization functionals are often introduced in the objective function to prevent overfitting. The specific context of this internship sits when the resulting minimization problem also involves inequality constraints on the model parameters or some functions of them. These constraints are important to avoid invalid or non-significant values for the model parameters.

Task:

Design and compare different numerical solving algorithms associated to different constrained nonlinear optimization methods.
Evaluate and compare these algorithms both formally and numerically using various (synthetic or real) benchmark cases including network tomography, network properties inference, etc. These tasks will be realized under supervision of senior (postdoc-level) researcher.

Duration: from 3 months to 1 year (max.)

Candidate profile:

if MSc thesis: the candidate must be following the last year of the curriculum in, e.g., Applied/ Numerical mathematics, Math/Mechanical engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
if internship: the candidate must have completed his MSc (in one of these disciplines). Copy of the MSc diploma/certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
Good knowledge of networked systems is considered as a plus.

Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Back

Apply

Back

Student in Nonlinear Inverse Problems

Leuven, Belgium

Student

Huawei Leuven Research Center

Inverse problems seek to reconstruct the unknown model parameters characterizing the system under investigation from measured data. More precisely, nonlinear inverse problems start by considering a nonlinear operator between the parameters, and acquired information / observation about them, i.e., data or measurements. This operator constructs a model for the available data, assumed to depend only on these parameters. Since the inverse problem is the inverse of the forward problem, this operator is often referred to as the forward operator. n nonlinear inverse problems, the forward operator being nonlinear, the model parameters depend in a nonlinear way of the state of the system and therefore on the observations available on it.

However, inverse problems are typically ill-posed, as opposed to the well-posed problems (verifying existence, uniqueness, and stability of the solution(s)). Also inverse problems are also typically ill-conditioned, i.e., a small change in the input induce a large change in the response. Hence, to tackles them, nonlinear regularization functionals are often introduced in the objective function to prevent overfitting. The specific context of this internship sits when the resulting minimization problem also involves inequality constraints on the model parameters or some functions of them. These constraints are important to avoid invalid or non-significant values for the model parameters.

Task:

Design and compare different numerical solving algorithms associated to different constrained nonlinear optimization methods.
Evaluate and compare these algorithms both formally and numerically using various (synthetic or real) benchmark cases including network tomography, network properties inference, etc. These tasks will be realized under supervision of senior (postdoc-level) researcher.

Duration: from 3 months to 1 year (max.)

Candidate profile:

if MSc thesis: the candidate must be following the last year of the curriculum in, e.g., Applied/ Numerical mathematics, Math/Mechanical engineering, Theoretical computer science, Computer science engineering. Detailed coordinates of MSc promotor and his/her academic affiliation must be provided in the CV application form.
if internship: the candidate must have completed his MSc (in one of these disciplines). Copy of the MSc diploma/certificate shall be included in annex of the CV. The internship can also be considered as part of post-MSc graduation or PhD graduation program.
Good knowledge of networked systems is considered as a plus.

Note well: candidate must have obtained their University degree from an academic institution of one of the EU country.

Starting date: Jan.1st, 2023 (earliest) - Sep.30, 2023 (latest)

Back

Apply

Back

Student in Nonlinear Mathematical Programming

Leuven, Belgium

Internship

Option for/Allows Remote Job

Huawei Leuven Research Center

Context

The Belgium Research Center growing activity on fast converging algorithms customized to the solving of large-scale optimization problems involving nonlinear coupling constraints / nonconvex composite objectives calls for multiple internship positions. These class of algorithms aim at addressing large-scale network optimization, i.e., network design, routing/traffic assignment, resource allocation, flow/resource scheduling, etc.

In essence, when solving (mixed-)binary or (mixed-)integer nonlinear programs with nonconvex composite objectives and nonconvex nonlinear coupling constraints, (convex relaxation coupled with) linearization methods often lead to lose continuous relaxations yielding untighten bounds. Hence, to tighten the continuous relaxation, the proposed algorithm shall combine the dynamic incorporation of both valid inequalities and optimality-based cuts within an outer—approximation-based branch-and-cut method to provide a tighter formulation. Note that reformulation of the original problem could also be considered to yield tighter outer approximation and secondly optimality-based cuts handled as lazy constraints. Moreover, in order to accelerate the execution, decomposition into independent subproblems can be further included as part of the algorithmic scheme. The convergence as well as the computational complexity properties of the method is to be formally characterized and proven.

Numerical executions on large-scale (mixed-)binary/(mixed-)integer nonlinear minimization problems using selected use cases from network optimization & distributed computing are essential to verify experimentally/numerically the computational performance and efficiency of the proposed method against state-of-the art. Particular effort shall be dedicated to prove the scalability of the proposed method with growing instance sizes.

Goal : together with the research & engineering team, the main objective of this internship is to design an OA B&C (outer-approximation branch-and-cut) algorithms and experiment them in combination with existing MIP (sub)solvers/tools such as Coin-OR Branch-and-Cut (CBC) by exploiting their callback functionality.

Tasks may include one (or more) of the following:

Extension of the OA-based decomposition scheme e.g. Proximal OA, Polyhedral OA, Quadratic OA, etc. and characterization of its mathematical properties.
Modeling and formalizing the handling of nonconvex nonlinear constraints for the OA based decomposition scheme and the OA-B&B/-B&C algorithmic scheme.
Design and development of methods / algorithms customized for network optimization problems involving nonlinear coupling constraints, nonconvex/nonlinear composite objective functions, etc. together with formal proof of their properties.
Experimentation and Integration in existing MIP solver/framework and use of subsolvers when required (MILP subsolvers such as CBC or NLP subsolvers).

Internship Position requirements

MSc degree (last year) or ongoing PhD degree in Applied mathematics, Mathematical engineering, Computational Mathematics/Algorithmic or Computer science (engineering).
- Note well: candidate must have obtained their University degree from an academic institution of one of the EU countries.
Good level and practical experience in either
- Continuous optimization / nonlinear programming (NLP) including iterative and projective methods
- or Mixed-integer nonlinear programming (MINLP) methods such as decomposition methods (OA, ECP, GBD) and B&B based methods (SBB, alpha B&B, OA B&B)
- Note: combinatorial optimization and integer/linear programming methods shall be considered as prerequisite for application
Good knowledge of programming language (C/C++, FORTRAN). Knowledge of numerical programming languages (Octave) is considered as a strong plus.
Excellent communication skills : presentation, oral and writing.

Seniority Level:

Entry level

Duration:

Fixed term: up to 2 years (renewable), possibility to combine with MSc Thesis.
Full-time (preferred) or part-time.

Starting date: May 1st, 2023 (earliest) - Sep.30, 2024 (latest)

Contact person:

Nicolas Spindelböck

HR Consultant

Back

Apply

Back

Student in Nonlinear Optimization

Leuven, Belgium

Student

Option for/Allows Remote Job

Huawei Leuven Research Center

In the context of context of communication networks, the solving of bound-constrained nonlinear optimization problems with, e.g., the Newton method or the Augmented Lagrangian Method (ALM), require the evaluation of the objective function, its gradient and the (sparsity pattern of the) Hessian matrix. Additionally, constrained optimization problems also involve providing the sparsity pattern and the Jacobian matrix of the constraints. The time and resource required to obtain this information and verify their correctness can be relatively large even for simple problems. Nowadays, optimization problem-solving environments provide modeling language and state-of-the-art optimization solvers together with packages that are capable to compute first-order information, e.g., derivatives, gradients. In this context, Automatic Differentiation (AD) remains largely unexploited for generating these quantities, requiring in turn to run the entire framework and its packages altogether to solve nonlinear optimization problems. Thus, it would also be beneficial to solve the corresponding nonconvex optimization problem (including nonlinear inequality constraints) by means of unified AD tools. Indeed, combining AD tools that automatically generate the required first- and second-order quantities together with nonlinear optimization methods such as ALM yields a promising method.

Objective: investigate and design nonlinear programming methods for solving nonconvex optimization problems with nonlinear constraints that can rapidly converge with few function and derivative evaluations. Ideally the proposed algorithm would also improve second-order information with the same efficiency and reliability as available for first-order information.

Task(s):

Formulate, develop and numerically evaluate enhancements/extensions of computational methods/algorithms including ALM for solving nonconvex optimization problems with nonlinear constraints.
To address the computational performance objectives, the candidate will also actively participate to the design (extension) and evaluation of (existing) automatic differentiation tools for solving such optimization problems.
Integration of these tools into a unified AD framework will be realized in cooperation with computational/numerical method experts. These tasks will be realized under the supervision of a senior (postdoc-level) researcher.

Duration:

Short duration (from 3 to 6 months): suited for MSc curriculum course
Long duration (up to 12 months): suited for MSc thesis internship

Candidate profile: MSc (or last year of MSc curriculum) in applied mathematics, mathematical engineering, theoretical computer science, or computer science engineering

Continuous optimization and related nonlinear programming techniques such as first-order iterative methods (PGD/PPD, ALM, ADM, etc.), inexact methods (Quasi-Newton methods, BFGS, etc.) and/or Krylov subspace methods ((B)CG, Lanczos, GMRES, MINRES, Arnoldi, etc.) for nonconvex optimization problems.
Experience in programming with nonlinear optimization libraries, e.g., LANCELOT/ CUTE, MINOS, TRON, IPopt, NLopt, etc. is considered as a strong plus.
Excellent written, verbal and interpersonal communication skills.

Application requirements

Certified copy of the MSc diploma/certificate shall be included in annex of the CV.
The CV shall indicate the detailed coordinates of the current or eventually the last academic institution and department of the candidate.
The CV shall include a detailed list of publications/achievements in relation to the job description.
Note well: the candidate must follow or have obtained his/her MSc degree from an academic institution of one of the EU countries.

Starting date: Jan.1st, 2023 (earliest) – Sep.30, 2023 (latest).

Back

Apply

Back

Student Network & Optimization MSc Mathematician

Leuven, Belgium

Internship

Partial Remote Job

Huawei Leuven Research Center

Huawei Gent Research Center

Huawei Belgium's Research Center is seeking Master Mathematicians to join our team on a fixed-term, unpaid internship basis for a minimum commitment of 3 months, up to a maximum of 6 months.

As an intern, you will work alongside our research and engineering team on advanced mathematical and computational optimization focused on network applications.

Your primary responsibilities will involve developing and evaluating mathematical methods and algorithms customized for network optimization problems, such as network design, routing/traffic assignment, resource allocation, and flow/resource scheduling.

Specifically, you will need to have a strong understanding of mixed integer (non)linear programming and at least one of the following optimization methods: iterative methods, projective/proximal methods, and combinatorial methods.

To succeed in this role, you must have excellent programming skills in Octave and either C/C++ or FORTRAN, as well as previous experience using optimization solving frameworks, such as Gurobi, SCIP, Couenne/COIN-OR, SHOT/COIN-OR, Algencan, Bonmin, and/or subsolvers, such as CPlex or Ipopt.

Additionally, candidates must have completed an MSc in Applied/Numerical Mathematics, Math/Mechanical Engineering, Theoretical Computer Science, or Computer Science Engineering from an academic institution in an EU country.

Candidates who are completing the last year of their MSc degree or who have completed an MSc may apply.

Those interested in pursuing an internship as part of their post-MSc graduation or PhD graduation program are also welcome to apply.

If you are a highly skilled Master Mathematician with a passion for optimization and network applications, we encourage you to apply for this internship opportunity with Huawei Belgium's Research Center.

Back

Apply

Back

华为-荷兰和比利时博士应届生投递渠道

Leuven, Belgium

Employee

Huawei Leuven Research Center

Huawei Gent Research Center

报名对象：华为诚邀2023年1月1日 - 2024年12月31日毕业来自荷兰和比利时各高校理工科方向博士同学投递简历。

主要投递渠道为：https://career.huawei.com/

如果你没有决定或者不清楚自己想要加入的部门，我们会根据投递同学简历和意愿匹配华为合适的部门

如申请者研究方向不在理工科范围内，华为也将以开放的姿态为优秀人才提供支持。

工作地点：中国\上海-上海中国\北京-北京中国\四川-成都中国\广东-东莞中国\广东-深圳中国\江苏-南京中国\江苏-苏州中国\浙江-杭州中国\湖北-武汉中国\陕西-西安

投递完成后请添加华为HR微信以遍后续联系：付煜 15588685200

Back

Apply

Back

华为-荷兰和比利时本科应届生投递渠道

Leuven, Belgium

Employee

Huawei Leuven Research Center

Huawei Gent Research Center

报名对象：华为诚邀2023年1月1日 - 2024年12月31日毕业来自荷兰和比利时各高校理工科方向本科同学投递简历。

主要投递渠道为：华为官网 https://career.huawei.com/

如果你没有决定或者不清楚自己想要加入的部门，我们会根据投递同学简历和意愿匹配华为合适的部门

如申请者研究方向不在理工科范围内，华为也将以开放的姿态为优秀人才提供支持。

工作地点：中国\上海-上海中国\北京-北京中国\四川-成都中国\广东-东莞中国\广东-深圳中国\江苏-南京中国\江苏-苏州中国\浙江-杭州中国\湖北-武汉中国\陕西-西安

投递完成后请添加华为HR微信以便后续通知：付煜 15588685200

Back

Apply

Back

华为-荷兰和比利时硕士研究生应届生投递渠道

Leuven, Belgium

Employee

Huawei Leuven Research Center

Huawei Gent Research Center

报名对象：华为诚邀2023年1月1日 - 2024年12月31日毕业来自荷兰和比利时各高校理工科方向硕士研究生同学投递简历。

主要投递渠道为：https://career.huawei.com/

如果你没有决定或者不清楚自己想要加入的部门，我们会根据投递同学简历和意愿匹配华为合适的部门

如申请者研究方向不在理工科范围内，华为也将以开放的姿态为优秀人才提供支持。

工作地点：中国\上海-上海中国\北京-北京中国\四川-成都中国\广东-东莞中国\广东-深圳中国\江苏-南京中国\江苏-苏州中国\浙江-杭州中国\湖北-武汉中国\陕西-西安

投递完成后请添加华为HR微信以便后续通知：付煜 15588685200

Back

Apply

In the picture

Caring for Employees

CURRENT OPEN JOBS

Spontaneous applications

ABOUT HUAWEI

Data Annotation Intern

Jr PhD Technical Engineer

Photonics Design Expert

Predictive Network Modeling Research Student

Predictive Network Modeling Research Student

R&D N&O Student

Spontaneous Application

Student in Composite Optimization

Student in Composite Optimization

Student in Computational Optimization

Student in Fast Converging Solvers

Student in Fast Converging Solvers

Student in Network Optimization

Contact person:

Student in Nonlinear Inverse Problems

Student in Nonlinear Inverse Problems

Student in Nonlinear Mathematical Programming

Contact person:

Student in Nonlinear Optimization

Student Network & Optimization MSc Mathematician

华为-荷兰和比利时博士应届生投递渠道

华为-荷兰和比利时本科应届生投递渠道

华为-荷兰和比利时硕士研究生应届生投递渠道