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12th October 2026 Languages English English Norsk Nynorsk English Postdoctoral Research Fellow position within Physics-Informed Machine Learning for Offshore Wind Apply for this job See
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Job Description Job Alerts Link Apply now Research Assistant (Computational Science and Machine Learning) University-Level Unit: College of Design and Engineering Faculty/Department-Level Unit
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: Design and implement ML, deep learning, and Large Language Models for orthopaedic applications Work with multimodal clinical data, including: Medical imaging (X-ray, CT, MRI) Electronic health records (HER
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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to inform service improvement. Help mental health services improve the support they offer to people with ongoing and severe mental health problems. Play a central role in the Mental Health Learning Systems
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within the digital twin environment Developing deep learning architectures for time-series forecasting, anomaly detection, and predictive maintenance of the physical asset Designing and training Physics
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, ideally with a background in graph-based or geometric deep learning. Written and oral command of English is essential. Research environment The Max Planck Institute of Biochemistry, Martinsried, was founded
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We invite excellent candidates in classical machine learning to join a collaborative research initiative between the MathEXLab of NUS (Mechanical Engineering) and the Centre for Quantum Technologies
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 19 hours ago
image processing, disease detection, diagnosis, and therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including
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mental health problems. Play a central role in the Mental Health Learning Systems Project. Explore how the experiences of people using services and the staff delivering care can be better understood and