Must have

• Machine learning

• Backbone

• Python

• C++

• Communication skills

• Machine Learning

• English

Requirements description

• PhD degree in Physics, Applied Mathematics, Computer Science, Statistics, Engineering, or other related fields.
• Good knowledge (with publication record) and experience in at least one of the following fields:
  • Optimization (numerical optimization, solver knowledge for problem classes such as QUBOs, (mixed) integer programming, linear programming, etc.)
  • Physical systems for optimization (reservoir computing, physical networks, annealers, Ising machines)
  • Dynamical systems, Complex networks, Optimal control theory
  • Numerical methods for ordinary/partial differential equations
  • Machine Learning (LLMs, autoencoders, etc.)
  • Backbone architectures for LLMs and diffusion models (deep theoretical and implementation-level knowledge)
  • Practical usage of Diffusion models (e.g., training, acceleration, distillation)
• Strong programming skills, with experience in one or more programming languages (including but not limited to Python, C++).
• Excellent teamwork spirit and communication skills.

Optional (priority given to candidates with experience in the following):

• High-performance computing (speed-of-light analyses of basic operations that can identify performance bottlenecks, stability properties (iso-efficiency), trade-off effects (e.g. memory vs. communication), etc. for operators that provide functionalities at a level similar to BLAS, SparseBLAS, GraphBLAS, LAPACK, LAGraph, etc.)

Offer description

Huawei is the leading provider of telecommunications equipment in the world with a strong focus on innovation. We have more than 70,000 employees engaged in R&D and have established 17 research institutes in countries including the U.S., Germany, Sweden, Finland, Poland, Russia, India, and China to ensure global R&D with outstanding people. We have set up over 20 joint innovation centers with top operators to transform leading technologies into a competitive edge for customers and achieve business success.

Researcher / Senior Researcher (Numerical Optimization, Complex systems, Control Theory, Statistical Physics)

What we offer:

• Full time office work in Wola, Warsaw
• Contract of employment, Agency agreement
• Private healthcare package. We offer premium private healthcare package for our employees.
• Sport Cards. Our employees can choose from many options within sport subscriptions and sport associations.
• Benefit Platform. You can choose your benefits on our Benefit Platform e.g.: cinema/theater tickets and discounts, shopping cards and many more.
• Special discounts for employees. We cooperate with various local companies to offer unique promotions only for our Employees.
• Office massages. It is a 15-minute chair-based massage. The massage therapist focuses on your back, neck, shoulders and arms.

Your responsibilities

1. Conduct applied research in the area of high-performance optimization, with strong focus on novel methods for hardware-accelerated solvers (such as simulated dynamical networks for combinatorial optimization, control theory for automated parameter scheduling, operator level analysis and reformulation of algorithms, statistical methods such as replicas for enhanced accuracy and optimized hardware usage, etc.)
2. Mathematical modeling of real-life optimization scenarios (such as objectives and constraints based on physical scenarios and product requirements)
3. Design, deploy, and test optimization algorithms related to the research targets with the goal to address concrete challenges from various applications and products within Huawei, such as (but not limited to) ML/AI, Wireless, Cloud computing.
4. Applied research with the goal of developing novel architectures in the field of machine learning with focus on generative AI, including implementation, training, and testing of models.
5. Define, lead, and drive projects, with a strong collaborative mindset towards other groups within Huawei
6. Initiate collaborations and projects with leading academic research groups in related fields.