Senior Computational Optimisation Engineer

Corintis develops cutting‑edge micro‑fluidic cooling solutions for AI chips, GPUs and CPUs that power today's data centres. By shaping the internal geometry of cold plates, the company helps its customers lower thermal resistance, reduce pumping power and increase compute‑per‑watt, addressing the massive electricity demand of data‑centre cooling. The firm, ranked Switzerland's top engineering startup for 2025, employs more than 105 staff from 35 nationalities and is located on the EPFL campus in St. Sulpice, a short walk from Lake Geneva. The Senior Computational Optimisation Engineer will create the optimisation tools that define the micro‑structures inside Corintis' cooling products. Reporting to the Head of Software Engineering, the role works closely with computational, product and manufacturing groups. Core duties include designing and coding numerical methods for topology optimisation, PDE‑constrained optimisation and multiscale material design, as well as formulating problems that involve homogenisation and effective material properties. The engineer will build and maintain simulation software in Python or C++, assemble workflows that combine finite‑element analysis, sensitivity and adjoint calculations, and apply gradient‑based optimisation. Additional tasks involve translating cross‑team engineering requirements into computational models, validating algorithms and results, documenting code and findings, and staying abreast of advances in topology optimisation and computational mechanics to integrate relevant innovations. The position demands a strong academic background and hands‑on expertise. Candidates must hold a Ph.D. in a discipline such as Mechanical, Aerospace, Civil, Computational Engineering, Applied Mathematics, Materials Science or Physics. They need proven experience with density‑based, level‑set or shape‑based topology optimisation, as well as with homogenisation techniques and multiscale modelling. A solid grasp of the finite‑element method and numerical PDE solvers is essential, together with practical knowledge of sensitivity analysis, adjoint methods and gradient optimisation. Proficiency in scientific programming using Python and/or C++ is required, alongside familiarity with version control, testing, documentation and modular code design. Experience with high‑performance computing, preferably using MPI, and fluent English communication skills are also mandatory. Corintis offers a collaborative, small‑team environment where ideas move quickly from concept to measurement. The on‑site role encourages close interaction with colleagues across disciplines, and the EPFL campus location provides easy access to the local innovation ecosystem and scenic Lake Geneva. The company values numerical accuracy, code review and reproducible results, and seeks individuals who enjoy tackling challenging mathematical problems that directly influence hardware performance. What the role asks for: - Ph.D. in Mechanical, Aerospace, Civil, Computational Engineering, Applied Mathematics, Materials Science, Physics or related field - Expertise in topology optimisation (density‑based, level‑set or shape methods) - Experience with homogenisation, multiscale modelling and effective material property computation - Strong background in finite‑element method and numerical PDE techniques - Experience with sensitivity analysis, adjoint methods and gradient‑based optimisation - Proficiency in Python and/or C++ for scientific software development - Knowledge of software development best practices (version control, testing, documentation, modular design) - Experience with high‑performance computing and parallel programming, preferably MPI - Fluent English with strong written and verbal communication skills - Nice‑to‑have: Preference for small on‑site team with rapid idea‑to‑measurement cycle.