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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio
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on the problem of making distributed machine learning robust to network outages and computational bottlenecks. The work is part of the Norwegian national AI centre SURE-AI, and the PhD student will
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing
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24th April 2026 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Distributed Machine Learning Apply for this job See advertisement This is
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properties. Our group uses theoretical and computational chemistry, physics, and materials science in combination with chemical machine learning to explore and exploit diverse functional organic and hybrid
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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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. Joerg Hoffmann at University of Saarland. 2) 1-2 PhD students working with Prof. Hendrik Blockeel and/or Prof. Jesse Davis on the topic of developing novel approaches for learning, compressing, and
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PhD position: Global soil mapping with process-informed machine learning Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline
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PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’
Vacancies PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’ Key takeaways In high-stakes crises, strategic teams often aim to avoid errors at all
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PhD candidate on Interprofessional Learning and Team Resilience through the Electronic Health Record
-proof healthcare? This PhD position offers the opportunity to explore how interprofessional teams can use insights from the Epic electronic health record to learn together, improve care, and strengthen