Master Student - AI Modeling for WSS and OCS Switching Systems

Huawei Research Center Germany; München; Vor Ort; EUR 13.000 - 20.000; Teilzeit

Benefits dieser Stelle

Canteen meals

Training opportunities

Language courses

Zusammenfassung

Huawei Heisenberg Research Center (Munich) invites Master’s students to work on AI-driven modelling for WSS and OCS switching systems. You will develop AI/ML surrogate models and inverse design frameworks to improve insertion loss, crosstalk, passband shape, and PDL.

The role includes building system‑level models, evaluating performance, and validating predictions against measurements. Candidates should be enrolled in a Master’s program in EE, Photonics, or CS with hands‑on experience in deep

Qualifikationen

• Pursuing a Master’s degree in EE, Photonics, Applied Physics, Optical Engineering, CS.
• Strong background in deep learning, neural networks, training/inference pipelines.
• Proficiency with PyTorch or TensorFlow.
• Familiar with inverse design, PINNs, reinforcement learning, surrogate modelling, or generative models.
• Experience with multi-objective optimisation using AI/ML methods.

Aufgaben

• Develop AI/ML surrogate models and inverse design frameworks for LCOS-based WSS optimisation, targeting insertion loss minimisation, crosstalk suppression, and PDL reduction.
• Build and refine WSS system-level models (optical path, LCOS switching engine, port architecture), evaluating insertion loss, crosstalk, passband shape, and PDL.
• Investigate WSS crosstalk suppression strategies through pixel layout design, phase/amplitude apodisation, and AI-driven optimisation.
• Develop AI-assisted models for OCS system-level performance, including switching speed, optical path stability, port-to-port uniformity, and insertion loss characterization.
• Validate AI model predictions against experimental measurements and simulation results.
• Study and apply modern deep learning architectures (e.g., CNNs, transformers, physics-informed neural networks, reinforcement learning) to the WSS and OCS design loops.

Kenntnisse

Deep learning

PyTorch or TensorFlow

Inverse design

PINNs

Reinforcement learning

Surrogate modelling

Generative models

Multi-objective optimisation

Ausbildung

Master's student in EE, Photonics, Applied Physics, Optical Engineering, CS

Tools

FDTD

RCWA

FEM

Jobbeschreibung

Huawei Heisenberg Research Center (Munich)

is responsible for advanced technology research, architectural development, design and strategic engineering of our products.

The Optical Technology Laboratory in Munich

researches and develops signal processing, components, systems, and optical switching for fixed access, data centre networks, metro and long-haul optical networks. The department engages in local research and collaborates with academic and industrial partners from the European ecosystem.

Join us as a

Master Student - AI Modeling for WSS and OCS Switching Systems (m/f/d)

Your mission

We are seeking a Master student to work on AI-driven modelling and optimisation of Wavelength Selective Switch (WSS) systems based on LCOS (Liquid Crystal on Silicon) technology and Optical Circuit Switching (OCS) systems. The student will develop AI/ML surrogate models, inverse design frameworks, and system-level optimisation pipelines to improve optical switching performance metrics including insertion loss, crosstalk, passband shape, switching latency, and polarization-dependent loss (PDL).

• Develop AI/ML surrogate models and inverse design frameworks for LCOS-based WSS optimisation, targeting insertion loss minimisation, crosstalk suppression, and PDL reduction.
• Build and refine WSS system-level models (optical path, LCOS switching engine, port architecture), evaluating insertion loss, crosstalk, passband shape, and PDL.
• Investigate WSS crosstalk suppression strategies through pixel layout design, phase/amplitude apodisation, and AI-driven optimisation.
• Develop AI-assisted models for OCS system-level performance, including switching speed, optical path stability, port-to-port uniformity, and insertion loss characterization.
• Validate AI model predictions against experimental measurements and simulation results.
• Study and apply modern deep learning architectures (e.g., CNNs, transformers, physics-informed neural networks, reinforcement learning) to the WSS and OCS design loops.

Your areas of expertise

• Enrolled in a Master's program in EE, Photonics, Applied Physics, Optical Engineering, CS, or related field.

AI / Machine Learning (Required)

• Strong background in deep learning fundamentals, including neural network architectures, training/inference pipelines, and GPU-accelerated computation.
• Proficiency with PyTorch or TensorFlow.
• Familiarity with one or more: inverse design, physics-informed neural networks (PINNs), reinforcement learning, surrogate modelling, or generative models.
• Experience with multi-objective optimisation using AI/ML methods.

Optics & Optical Design (Required)

• Strong foundational knowledge of physical optics, including wave propagation, diffraction, interference, polarisation, and beam optics.
• Understanding of liquid crystal optics and LCOS device operation principles.
• Understanding of WSS architecture and key performance metrics: insertion loss, crosstalk, bandwidth, PDL, port count, passband shape.
• Understanding of OCS architecture and key performance metrics: switching speed, port count, insertion loss, crosstalk.
• Ability to perform or interpret simulations (FDTD, RCWA, FEM) of optical structures.

Nice-to-Have (Plus)

• Knowledge of metasurface operating principles (phase/amplitude/polarisation control via subwavelength structures), unit-cell design, lattice types, and phase-gradient concepts.
• Experience with LC device modelling (Frank‑Oseen theory, director simulation).
• Knowledge of DWDM systems, ROADM architectures, and telecom optical component design.
• Familiarity with data centre optical interconnects and OCS-based network architectures.

Prior work at the intersection of AI and photonics/optics

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Your rewards of working here

• Our culture is characterised by innovative power and team spirit as well as the intensive exchange of knowledge and experience within our global network.
• We offer healthy meals ranging from traditional Chinese to western delicacies in our famous company canteen.
• To keep your development ongoing, you will find a broad range of training opportunities. Many online and face-to-face training programmes incl. language courses in German and Mandarin.
• Our diverse and welcoming environment is shaped by different backgrounds and around 40 individual nationalities.
• Self-responsible work in a competent, motivated and constantly growing team.

Huawei is a leading global information and communications technology (ICT) solutions provider. Our ICT solutions, products and services are used in more than 170 countries and regions, serving over one-third of the world's population. With 208,000 employees, Huawei is committed to develop the future information society and build a Better Connected World.