Senior Deep Learning Research Scientist

Senior Research Scientist, Geometric Deep Learning and Scientific MLLocation: Berlin, GermanyWorking model: Full time, hybridAbout the companyWe are supporting an early stage deep learning company developing foundation models for engineering and physical systems.The company works with industrial partners across mechanical engineering, electrical engineering, manufacturing, and engineering design. Its goal is to create models that can generalise across different physical problems, geometries, and industrial constraints.This is a small research led team where new ideas can be implemented, tested, and improved quickly.The opportunityWe are looking for a Senior Research Scientist who can develop original deep learning methods for complex engineering problems.This is not a role focused on applying existing models without questioning them. You will be expected to understand why an architecture works, identify where current methods fail, and develop new approaches from first principles.You should have deep research expertise in at least one relevant area, while being willing to work across adjacent fields as projects develop.What you could work onDesigning and validating new geometric deep learning architecturesDeveloping models for graphs, meshes, point clouds, particles, surfaces, and other structured dataBuilding generative models for engineering design and physical systemsDeveloping surrogate models for computationally expensive simulationsTraining models using synthetic and simulated dataConditioning generative models on multimodal inputs and physical constraintsExploring neural operators, neural differential equations, diffusion models, and function space modellingStudying network architecture, optimiser behaviour, regularisation, loss geometry, and generalisationTranslating research papers into reliable experimental systemsWorking directly with industrial simulation environments and proprietary engineering datasetsWhat we are looking forA PhD in computer science, mathematics, applied mathematics, physics, engineering, or a related subjectResearch depth in geometric deep learning, scientific machine learning, generative modelling, synthetic data, surrogate modelling, neural operators, or a closely related areaStrong mathematical understanding of neural networks, optimisation, and generalisationEvidence that you can develop original methods rather than only reproduce existing researchExperience designing, training, and evaluating deep learning architecturesAbility to read papers critically and explain the reasoning behind technical decisionsStrong Python experience with PyTorch, JAX, TensorFlow, or similar frameworksInterest in applying research to difficult industrial problemsCandidates completing a strong PhD, experienced postdoctoral researchers, and researchers with several years of industry experience are all encouraged to apply.Useful additional experienceEquivariant neural networksGraph neural networksThree dimensional geometry, meshes, point clouds, or particle systemsPhysics informed learning and differentiable simulationSynthetic data generationReinforcement learning for optimisation or trajectory generationProcedural geometry generationSimulated data transfer into real applicationsExperience moving research models into production environmentsPrevious CAD, CAM, CNC, or manufacturing experience is not required.What is offeredDirect influence over the company’s research directionClose collaboration with the foundersFreedom to explore and test original ideasAccess to industrial datasets and simulation environmentsShort research and development cyclesConference attendance and continued learning supportFlexible working hoursPotential equity participationPossible relocation supportEnglish speaking working environment, with no German requirementThe team works from Berlin, with approximately two to three days per week available to work from home.If you enjoy developing new deep learning methods, reasoning from mathematical foundations, and applying research to physical engineering problems, we would be interested in speaking with you.