Computational Optimisation Engineer

Corintis develops micro‑fluidic cooling solutions for AI chips, GPUs and CPUs used in data centres, aiming to cut the massive electricity demand of current cooling systems. As a Computational Optimisation Engineer you will create the optimisation tools that define the internal geometry of the company’s cold‑plate products, reporting to the Head of Software Engineering and collaborating with computational, product and manufacturing groups. Your daily work will involve designing and coding numerical methods for topology optimisation, PDE‑constrained optimisation and multiscale material design, as well as formulating problems that incorporate homogenisation and effective material properties. You will build and maintain Python or C++ software that links finite‑element analysis, sensitivity and adjoint calculations, and gradient‑based optimisation, while validating models, documenting code and presenting results to both technical and non‑technical colleagues. Keeping abreast of advances in topology optimisation and computational mechanics and integrating relevant techniques into the code base is also expected. The position requires a Ph.D. in a relevant engineering or scientific discipline, deep knowledge of topology optimisation (density‑based, level‑set or shape methods), homogenisation, multiscale modelling, FEM and PDE solvers, plus experience with sensitivity analysis, adjoint methods and gradient optimisation. Proficiency in Python and/or C++, familiarity with version control, testing and modular design, and experience with high‑