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integrate linear and circular processes, enabling used products to be transformed into new generations. What you will do Implement GPU-accelerated Gaussian Mixture Model (GMM) learning in PyTorch Optimize
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: metrics, configs, checkpoints, weight versioning, model registry Simulation and Testing: Run large-scale cloud experiments; track throughput, GPU utilization, cost per run; evaluate robustness to preemption
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science, mathematics, statistics, computational linguistics, physics, electrical engineering, or similar with good grades PyTorch skills: experience in training machine learning models with one or more GPUs; ability to
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Gaussian Mixture Model (GMM) learning Contribute to implementation, optimization, and benchmarking tasks in GPU-accelerated environments Assist in preparing experimental results and documentation
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, OpenFOAM), and plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov
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: – Knowledge of high-performance computers and GPU computing – Knowledge in data protection, especially with medical, personal data – Knowledge in operating ticket systems – Knowledge in creating wiki pages (e.g
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environment with strong expertise in immunotherapies An open, collegial, and supportive working atmosphere in a respectful organizational culture A highly diverse and inclusive workforce Access to our GPU
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the use of and scientific application programming for supercomputers Knowledge in GPU-based programming and modelling of scientific simulations are desirable Programming experience in C, C++, or Fortran is
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and train CNN and SNN models utilizing frameworks such as Keras, PyTorch, and SNNtorch Implement GPU acceleration through CUDA to enable efficient neural network training Apply hardware-aware design
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-unterstütze Simulation« team offers you exactly that. What you will do Optimizing existing code for electronics application considering multi-CPU and multi-GPU usage (implementation in jax and/or numpy and/or C