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Field
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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method, can engineer thousands of defined mutations in parallel in a single test tube in yeast. Strains are tagged by DNA barcodes, allowing to efficiently track mutations in cell populations during
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on a flat-belt conveyor and are dynamically grasped, unfolded, and oriented by multi-axis manipulators fitted with custom force-feedback grippers, and will work in close concert with a parallel sensing
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optical computing devices that can solve hard combinatorial problems. Such combinatorial problems are commonplace in our society, for instance in logistics, finance or pharmaceutical research. However, for
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volcanic environments. Development and flight testing will run in parallel with field campaigns at three key volcanic sites: Soufrière Hills Volcano (Montserrat), Fuego (Guatemala), and Lascar (Chile
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computing clusters, parallel processing, and data pipelining. Strong analytical and statistical background, with the capability to interpret complex biomedical data accurately. Direct experience handling and
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increasingly complex compute- and data-intensive problems in science and engineering on high-end parallel and distributed computing platforms. More information: https://sc.cs.univie.ac.at/ Your future tasks
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. This role will support the Edge Compute and Satellite Storage Service, within the Research Platforms Team, within RIT. It is primary focusing on supporting the National X-Ray Computed Tomography (NXCT
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to effectively parallelize code, in addition to proficiency in Python and general software engineering skills. Improve software quality for non-functional requirements, such as readability and performance
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source