Senior Computational Physicist – Numerical Methods
Supporting VP's, CTO's and Engineering Managers to seek and hire the best talent within the European Semiconductor and RF domain.
Join a leading research-driven technology company developing next-generation computational imaging solutions. As a Senior Computational Physicist – Numerical Methods, you’ll design, implement, and optimise advanced numerical solvers for large-scale PDEs, inverse problems, and real-time scientific applications within high-performance computing environments.
What You’ll Do
* Develop, optimise, and maintain large-scale solvers for PDEs and inverse problems applied to imaging and scientific applications.
* Enhance solver performance across CPU/GPU platforms, focusing on parallelisation, memory management, and real‑time execution.
* Integrate numerical solvers into production workflows in collaboration with software, algorithm, and engineering teams.
* Provide technical leadership, mentor junior researchers and developers, and drive solver architecture decisions.
* Ensure robustness, scalability, and high-performance of computational methods across diverse scenarios.
* Contribute to long‑term R&D strategy and innovations in solver design and high‑performance computing techniques.
What You’ll Need
* MSc or PhD in Applied Mathematics, Computational Physics, Electrical Engineering, or a related discipline.
* 5+ years of experience in scientific computing with strong expertise in numerical PDE solvers and computational methods.
* Proficiency in C/C++ and Python for scientific computing, with experience in debugging, optimisation, and validation of complex code.
* Familiarity with HPC frameworks and libraries (BLAS, LAPACK, CUDA, OpenCL, MPI).
* Experience applying machine learning frameworks (e.g., PyTorch, TensorFlow) to physics‑based or computational problems.
* Proven track record leading computational projects in cross‑functional, collaborative environments.
Preferred
* Background in real‑time or embedded computing for scientific/medical applications.
* Experience with inverse problems, PDE‑constrained optimisation, or ML‑enhanced solvers (e.g., physics‑informed neural networks).
* Understanding of FPGA/ASIC architectures or digital circuit design (Verilog/VHDL).
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function / Industries
Staffing and Recruiting, Medical Equipment Manufacturing, and Appliances, Electrical, and Electronics Manufacturing
Referrals increase your chances of interviewing at Umbilical Advanced by 2x
Get notified about new Scientific Staff jobs in Vienna, Austria.
Innere Stadt, Vienna, Austria
#J-18808-Ljbffr