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Field
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Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience with high-performance computing (HPC
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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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modelling, preferably WRF Experience of scientific programming and running code on HPC systems Experience with Fortran, Python and Linux Shell Any of the following is advantageous but not essential
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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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/TranSIESTA), or Molecular Dynamics (including hybrid QM/MM or ML-IP simulations) - Apply for computational resources in HPC facilities when needed. - Prepare periodic reports of the results and provide
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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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conversion systems (e.g., gas turbine combustors, detonation engines, reciprocating engines, etc.) using CFD solvers (e.g., CONVERGE, Nek5000/NekRS, OpenFOAM, ANSYS Fluent, etc.) on large-scale HPC platforms
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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