PpbFacing the challenges of our time - /bHelp 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. The “Integrated and Wireless Systems” Business Unit, based in Neuchâtel, Switzerland is looking for a motivated intern to contribute to the AI and machine learning aspects of these efforts. /p h3Your mission /h3 pYou 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: /p ul liInverse design of nonlinear photonic waveguides for supercontinuum generation, using surrogate-based and Bayesian optimization to explore large design spaces. /li liAI-assisted antenna design, combining electromagnetic simulations with data-driven modeling to address multi-objective constraints and explore complex design spaces. /li liPhysics-informed neural networks (PINNs) for photonic simulation, developing neural surrogates that embed Maxwell’s equations to accelerate conventional solvers. /li /ul h3Your responsibilities /h3 ul liImplement and benchmark ML optimization pipelines (e.g., Bayesian optimization, surrogate modeling, evolutionary algorithms) interfaced with physics simulators. /li liTrain and evaluate neural network models (e.g., PINNs, neural operators) on photonic or electromagnetic simulation data. /li liAnalyze results, compare AI-generated designs against human-designed baselines, and document findings. /li liCollaborate with domain experts in photonics and electromagnetics to ensure physical consistency of ML outputs. /li liContribute to internal technical reports and, if results warrant, to conference or journal publications. /li /ul h3Your profile /h3 pbRequirements /b /p ul liCurrently enrolled in a Master’s program in electrical engineering, physics, computer science, or a related field. /li liSolid foundation in machine learning and deep learning (coursework or project experience). /li liProficiency in Python and at least one deep learning framework (PyTorch or TensorFlow). /li liFamiliarity with Git and Linux environments. /li liGood communication skills in English; French is a plus. /li /ul pbPreferred qualifications (one or more) /b /p ul liExperience with optimization methods (Bayesian optimization, genetic algorithms, or gradient-based optimization). /li liExposure to physics-informed or scientific machine learning. /li liBackground in photonics, electromagnetics, or computational physics. /li liPrior experience with simulation tools (e.g., COMSOL, or equivalent). /li /ul pbInterpersonal skills /b /p ul liCurious, self-driven, and comfortable working in a multidisciplinary environment. /li liGood problem-solving abilities and a hands‑on, results-oriented approach. /li liStrong collaboration and communication skills. /li /ul pWe are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity. /p /p #J-18808-Ljbffr