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to learn A background in renewable energy, e-mobility, or engineering Good programming skills (e.g., Python, MATLAB or R) Familiarity with control concepts Ability to work effectively with data and applied
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machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy. Information In the manufacturing landscape, determining whether a component should be
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for this position focused on machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in collaboration with Brookhaven Lab staff, the Department of Energy (DOE), and
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
arithmetic cores for FPGAs). The team hosts 6 faculty, 6 PhD students, 3 postdocs, 2 engineer, and multiple research interns. Additional information can be found on team website: https://team.inria.fr/emeraude
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PhD candidates, postdocs, and faculty members. Our group focuses on understanding and mitigating corrosion processes, and on the development of electrocatalysts and electrochemical sensors through
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preprocessing IoMT network traffic datasets. Implement and evaluate machine learning algorithms (e.g., logistic regression, SVM, random forest) for intrusion detection. Develop prototype software tools (e.g
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close collaboration with the Netherlands Defence Academy. The CTE group is a dynamic team of researchers, including PhD candidates, postdocs, and faculty members. Our group focuses on understanding and
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, a protein expressed in many cancers, which is an indispensable element of the meiotic DSB formation machinery, but which exact function is not known. In order to learn more about MEI4 and its partners
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real