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will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core responsibility is to build and validate these offloading strategies, complete with Python
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polarization states. • Data Analysis & ML: Develop Python/MATLAB machine learning algorithms to map photocurrents to full Stokes parameters, targeting a reconstruction error of \(<1\%\). Required
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-on programming skills in R and Python, acting within server-side development environments such as RStudio, JupyterLab, and RSConnect, and will document development decisions and contribute to knowledge-sharing
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reports. Job Requirements: PhD in Physics, Applied Physics, Plasma Physics, or related field. Demonstrated experience in scientific computing and numerical modelling (e.g., Python/C/C++/Fortran). Experience
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pipelines in R, Python and HPC or cloud environments. Collaborate with developmental biologists to interpret genomic findings, validate results and address key evolutionary questions. Support and mentor PhD
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knowledge in building ventilation systems, heat transfer, fluid dynamics, machine learning, data analysis, and coding e.g., Python. Understand sensors, actuators, data acquisition, and control systems
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and experience in one or more open-source languages (e.g. Python, R). Prior experience/knowledge of computational psychiatry methods for modelling cognitive tasks. Experience in the use of neuroimaging
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to biological, imaging, or clinical data. Proficiency in ML frameworks (experience with LLavaMed, EchoPrime and DNABERT considered an asset) and programming languages such as Python or R. Experience working with
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Organization U.S. Department of Agriculture (USDA) Reference Code USDA-ARS-MWA-2026-0235 How to Apply To submit your application, scroll to the bottom of this opportunity and click APPLY. A complete application consists of: An application Transcript(s) – For this opportunity, an unofficial...
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modeling. Proficiency in Python and experience with AI/ML frameworks such as PyTorch or TensorFlow. Experience handling large geospatial, atmospheric, or remote sensing datasets is preferred. Strong