[Integrated and lightweight photovoltaics]

Facing the challenges of our time

Help us grow and be more impactful!

• CSEM is advancing the use of artificial intelligence to accelerate the design of complex physical systems. Several ongoing projects apply ML-driven optimization and physics-informed modeling to engineering domains including photonic integrated circuits, electromagnetic antenna design, and photonic simulation. based in Neuchâtel, Switzerland is looking for a motivated intern to contribute to the AI and machine learning aspects of these efforts
• You will support a multidisciplinary team in applying machine learning to inverse design and simulation problems. The internship sits at the intersection of AI and physics-based engineering: you will help develop, train, and benchmark ML models that optimize device geometries or accelerate physical simulations. Depending on your profile and interests, your work may focus on one or more of the following application areas:
• for supercontinuum generation, using surrogate-based and Bayesian optimization to explore large design spaces
• Inverse design of nonlinear photonic waveguides
• combining electromagnetic simulations with data-driven modeling to address multi-objective constraints and explore complex design spaces
• developing neural surrogates that embed Maxwell’s equations to accelerate conventional solvers
• Physics-informed neural networks (PINNs) for photonic simulation

Implement and benchmark ML optimization pipelines (e.g., Bayesian optimization, surrogate modeling, evolutionary algorithms) interfaced with physics simulators