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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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/359354/2-phd-positions-on-learning-cau… Requirements Additional Information Website for additional job details https://www.academictransfer.com/359354/ Work Location(s) Number of offers available2Company
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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Reflectometry The aim of the PhD project is to provide machine learning (ML) based neutron reflectometry (NR) analysis as an automatized workflow for the reflectometry instruments at the Institut Laue-Langevin
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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for a full-time, on-site PhD position in machine learning, forecasting and time series analysis. Reykjavik University, Department of Engineering. Duration: 3 years. Start date: Negotiable. Reykjavik
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high-quality research in one of the Department's key research areas: (i) Artificial Intelligence and Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition
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foundations, quantum information theory, and quantum technologies. For additional information, please visit: https://dakic.univie.ac.at/ . Your future tasks: You will actively participate in research, teaching