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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance
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commuting to the assigned work location when necessary. Qualifications We Require: PhD in engineering, physics, applied mathematics, computer science or other relevant field Ability to obtain and maintain a
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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: Candidates must have a MS degree or PhD in Nuclear engineering, computer
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computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
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: Recent PhD and/or MD in a relevant field, or equivalent research experience, with a background in molecular biology, transcriptional regulation, functional genomics, computational biology, genetics
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at least one of: High-Performance Computing Distributed Systems Parallel Computing High Performance Algorithms Multiple Linux distributions · Experience with SLURM or similar HPC schedulers
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State University of New York University at Albany | Albany, New York | United States | about 1 hour ago
vibration isolation). In parallel to the benefits of quantum computing, artificial intelligence and neuromorphic computing seek to emulate the massively parallel, highly efficient computing capacity
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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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distributed computing for EMT simulations. • Experience with software development in Python, C++, or other programming languages. • Familiarity with GPU acceleration of numerical solvers, parallel sparse