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for machine learning/AI model development development using Pytorch, Tesnor Flow or similar platforms. Utilization of high-performance computational platforms. Writing and modifying scientific code in Fortran/C
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intereactions in the broad field of glycoprotein-based therapeutic design. Strong background and experience with applications of machine learning (especially Explainable Artificial Intelligence – XAI) is required
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Apply Now Job ID JR100614Date posted 10/12/2023 The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates
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The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for a post-doctoral research associate
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Posting Title Postdoctoral Researcher – Machine Learning for Protein Design and Engineering . Location CO - Golden . Position Type Postdoc (Fixed Term) . Hours Per Week 40 . Working at NREL
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applying the latest advancements in machine learning, high performance computing, and numerical methods to develop computational tools and models that are used for large-scale, physics-based simulations of a
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learning and its applications in nuclear engineering, national security, nuclear safeguards, or relevant fields. The successful candidate will participate in ongoing research in machine learning
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Berkeley Lab’s (LBNL ) Energy Geosciences Division (EGD) has an opening for a Postdoctoral Researcher in Machine Learning for Geological Carbon Storage to join the team. In this exciting role, you
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Postdoctoral Research Associate - Structural Simulation and Machine Learning (ML) for Polymer Compos
manufacturing technologies through machine learning and physics-based simulations, specifically finite element analysis (FEA) for polymer composites. The candidate will also focus on developing a manufacturing
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) is seeking qualified applicants for a postdoctoral position in machine learning and surrogate models. Areas of interest include graph neural networks, federated learning, data-driven model reduction