Senior Scientific Computing Engineer

Vor 6 Tagen

Wien, Wien, Österreich Umbilical Advanced Vollzeit € 80.000 - € 140.000 pro Jahr

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).

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