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, or AI inference combined with traditional FPGA development workflows. Familiarity with AMD Versal SoC architecture and the Vivado/Vitis tool chains. Basic understanding of x-ray or neutron scattering is
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machine learning algorithms on HPC architectures Special Requirements: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree
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. Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). A broad understanding of machine learning methodologies and
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, or AI inference combined with traditional FPGA development workflows. Familiarity with AMD Versal SoC architecture and the Vivado/Vitis tool chains. Basic understanding of x-ray or neutron scattering is
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, and measure success. Basic Qualifications: PhD degree in a related scientific field (e.g. architecture, architectural/mechanical/electrical engineering) completed within the last five years. Proven
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include the porting of lattice QCD libraries to the Exascale Architectures (e.g. Frontier at OLCF) and/or emerging programming models (HIP, SYCL, Kokkos, etc), software optimization and/or algorithmic
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software for the simulation of electrochemical systems for applications such as material synthesis, electrochemical reduction and separation processes. Participate in the design and architecture
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, architecture, construction, or closely related field completed within the last 5 years. Experience and demonstrated ability to use Microsoft Excel and basic programming skills in C, C++, Python, or Matlab
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Experience in the design and implementation of scalable numerical algorithms on HPC architectures Experience developing mathematical tools for engineering or science applications Special Requirements