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languages; experience with GPU programming (e.g., CUDA) is highly desirable. Background in optimization, image-guided radiotherapy, medical imaging, or computational modeling. Experience with treatment
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engage with collaborators across Belgian universities. Profile Education: PhD in Artificial Intelligence, Bioinformatics, Computer Science, Physics, Engineering, or a related field. Programming
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class
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in top-tier machine learning/AI conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch
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to the instalment of translational programs and how these processes are hijacked in human disease. Due to the central role of the ribosome in many biological processes, we use a broad panel of model systems
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with in-depth knowledge of parallel programming (GPU, multi-threading, etc.). - Familiarity with standard collaborative development tools: Git, GitHub, CMake, Guix-HPC, Spack, GTest, CTest, etc
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: Knowledge on floating point arithmetic and mixed/reduced precision computing techniques Experience with programming GPUs and/or other accelerators Proficiency in mathematical reasoning and numerical analysis
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties