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Field
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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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, Azure) Exposure to GPU computing for data analysis Knowledge of AI/ML approaches relevant to omics data Practical understanding of biostatistics and statistical modeling Experience delivering
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in data science, computer science, computational biology, or a related field Solid programming and data science skills, including familiarity with Linux, Python programming, version control, continuous
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 11 days ago
, synchronization, scalability, and familiarity with GPU programming; strong familiarity with Unix/Linux tools; solid experience with version control, debuggers, compilers, and profiling tools (e.g., perf, gprof
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molecular dynamics simulations and was specially designed for parallelisation on GPUs. It is open source and licensed under the LGPL. Details can be found on the website https://halmd.org Job-Description
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metrics and usage statistics, identify inefficiencies on different levels (CPU/GPU, I/O patterns, etc.) and provide corresponding reports. You will work closely with researchers and HPC users and provide
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programme of computer science, mathematics, physics, electrical engineering, computational linguistics, or similar with good grades PyTorch skills: experience in training machine learning models with one
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research. Strong technical expertise in large scale data analysis, statistical and simulation tools, machine learning, AI and GPU programming. Experience with one more of these scientific collaborations
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computing resources, with additional support involving HPC systems such as configuring GPU nodes, managing Slurm queues, containerising teaching notebooks, and enabling advanced pipelines Promote Robust
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one of the above fields Very good expertise in the programming languages Python and C/C++, the numba library, and in applying parallelization techniques using GPU programming (CUDA/OpenCL) and MPI