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learning packages (e.g. PyTorch, Keras) Experience with HPC and scientific workflow management tools (e.g. Nextflow, Snakemake) Experience with single-cell data analysis (e.g scanpy, scvi), and/or spatial
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experience with genomic data. You are confident use of HPC environments, version control and FAIR principles. You have excellent English communication skills and a strong publication record. You fit to us: if
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, and optimize for energy efficiency HPC applications and high performance data stream analytics workloads. Use of novel accelerator designs, and automatic methods to model/predict how performance would
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engineering team to translate the models into production. The successful candidate will be part of a cross-lab, highly inter-disciplinary team of experts in ML, applied math, HPC, signal processing
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-house codes and making use of high-performance computing (HPC) tools. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical, aerospace
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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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scientific computing. Experience running large-scale excited-state simulations on HPC platforms. Excellent record of productive and creative research as demonstrated by publications in peer-reviewed journals
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of the field through the development and use of machine learning, deep learning, and high-performance computing (HPC). This position resides in the Chemical Separations Group in the Separations and Polymer
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response Demonstrated expertise in process development/optimization for macro-scale deformation in AM Experience with multi-physics simulations on high performance computing (HPC) and maching learning (ML
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, and image analysis tools and working on a computing cluster (HPC). Vivid Interest in interdisciplinary research, leading and working on projects with pathologists, medical experts, computer scientists