573 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "NORTHUMBRIA UNIVERSITY" positions in Norway
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, and public health. Read more about UIO:RWE here: https://www.uio.no/english/research/strategic-research-areas/life-science/research/convergence-environments/uiorwe About the project This postdoctoral
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for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned
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(ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning
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Magnets for New Energy and MObility Applications“ funded by the European Union (ERC, MagNEO, 10522110, https://magneoproject.eu/ ). The project aims to develop new alloy material for permanent magnets
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advertisement Job description A three-year PhD fellowship in Neuroscience is available in the group of Dr. Matthijs Dorst (https://dorst-lab.org/ ), with co-supervision by Associate Professor Koen Vervaeke (https
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: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: The average grade point for courses included in the Bachelor’s degree must be C
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to inflammatory joint diseases. For complete job description with qualification requirements and basis of evaluation on the academic position please see: https://www.med.uio.no/klinmed/om/ledige-stillinger
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will be the Head of Department. About the project The successful candidate will be part of the project SAPPHIRE (https://sapphire.norceresearch.no/ ). SAPPHIRE aims to contribute to cleaner and more
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. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with an emphasis on pattern identification, prediction, or spatial risk mapping