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The design of industrial membrane separation materials requires advanced computation methods, such as computational fluid dynamics and computational chemistry, to design, analyze, and predict
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requires an expert who can architect modern high-performing, secure cloud and hybrid integration platforms while actively writing code, creating reusable frameworks, and mentoring engineering teams. You will
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to improving the quality and efficiency of the consortium code base. Preference will be given to candidates with a strong publication record and proven experience in Python programming, source code versioning
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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is to develop a modeling framework including the use of Random-Walk method to predict NMR measurements, pore-scale finite-element modeling on 3D digital models, generated from CT-images to predict
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. Contribute to writing methods sections and developing reproducible analysis scripts. 3. Technical Infrastructure Manage code repositories and collaborative workflows using GitHub/GitLab. Maintain documentation