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-supervised machine learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to train models on synthetic data generated by biophysical
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fields, and machine-learning-force fields. The initial appointment is for one year, with the possibility of renewal contingent upon satisfactory performance. For additional information about this
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optimization, machine learning, control theory, or stochastic modeling. Crucially, the ideal candidate must have a proven track record of viewing civil infrastructure through a holistic, interconnected
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English. Ability to work independently as well as collaboratively in an interdisciplinary team. Experience with health/real grid data or machine learning is beneficial but not mandatory. We Offer One year
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plus Experience in modeling, simulation and machine learning Basic knowledge of control engineering Experience in reinforcement learning is not required but highly beneficial Very good command of
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data-driven methods to understand, design, and optimize complex energy systems and devices. Example topics include physics-informed machine learning, digital twins for turbines and reactors, AI-driven
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rigorous evaluation methodology for interpretability claims, i.e., faithfulness, stability, and causal grounding. Research, design, implement, and apply advanced machine learning methods for multiple
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 13 hours ago
learning using 3D data. More information about the research group and KAUST can be found under the following links: http://peterwonka.net/ https://cemse.kaust.edu.sa/vcc https://www.kaust.edu.sa/en If you
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advanced machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a