Sort by
Refine Your Search
-
- into a GPU-enabled and parallel code to run efficiently on state-of-the-art exascale hardware Designing implementations and reviewing community contributions of library features and new statistical
-
: 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
-
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
-
Gaussian Mixture Model (GMM) learning Contribute to implementation, optimization, and benchmarking tasks in GPU-accelerated environments Assist in preparing experimental results and documentation
-
, 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
-
: – 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
-
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
-
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
-
or more GPUs; ability to work with pre-existing codebases and get a training run going Research interest in one or more of the following: Applied ML, Natural Language Processing, Computer Vision
-
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