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doctoral/Ph.D. studies in Human Medicine, Veterinary Medicine, Biology, or a related discipline Interest in or initial experience with laboratory management and the coordination of multiple parallel projects
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heterogeneous (CPU/GPU) computing models. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and
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of Power Engineering; Xiaozhe Wang ECSE 408: Communication Systems; Ioannis Psaromiligkos ECSE 412: Discrete Time Signal Processing (cross-listed with ECSE 512); Benoit Champagne ECSE 420: Parallel Computing
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100%, Zurich, fixed-term The Advanced Manufacturing Lab (am|z) at the Department of Mechanical & Process Engineering (D-MAVT) at ETH Zurich develops advanced manufacturing methods and systems
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Description We work with scientists in other NIST laboratories to develop tools for computer simulation and analysis of magnetic systems at the nanometer scale. Model verification is achieved by comparison
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to manage multiple projects in parallel. Ability to analyze and interpret statistical data and to communicate data in a clear, concise manner. Computer literacy, including the ability to use a variety of
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strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high
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of parallelized fibers around which a binding thread is helically wound, enables alignment of the fibrous core while ensuring its cohesion. This configuration promotes better matrix accessibility and more
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to anticipate changes in water availability. Addressing this challenge requires new modelling approaches capable of representing groundwater processes across thousands of heterogeneous catchments. To tackle
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handling. Analytical, reasoning and problem-solving skills. Ability to work in an intense and deadline-focused environment. Ability to manage multiple projects in parallel. Ability to analyze and interpret