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Field
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computer science or a related discipline. Good knowledge of agentic AI/AI workflows, parallel computing, algorithms, and system performance analysis and optimization. Proficiency in programming languages
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to closely collaborate with CS faculty members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience
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guidance of two experts in computational microscopy and machine learning systems, blending expertise in advanced bioimage informatics and parallel computing: Dr. Min Xu – Affiliated Associate Professor
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related experience. Unix skills. Experience with scripting and automation (e.g., R, Bash, Python) Understanding of the principles of computer clusters – parallel computing, resource allocation, multiple
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. Keywords Image processing; Machine learning, Computer vision; Statistical methods; Parallel and distributed computing; Software engineering; Standards; Eligibility citizenship Open to U.S. citizens level
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parallel codes. Experience with using high performance computing for lattice model simulations. Experience working in a multi-disciplinary research environment. Demonstrated written and oral
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commercial CFD codes such as STAR-CCM+, ANSYS or COMSOL. Knowledge of finite element simulations methods. Experience using parallel Linux computing platforms, parallel job submission scripts, common
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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learning techniques. Adapt computational pipelines for efficient execution on a high-performance computing (HPC) environment, including workflow automation and parallel computing. Apply these computational
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postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel