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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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related field Strong background in data analysis, particularly with behavior data, functional ultrasound (fUS) or other neuroimaging modalities Proficiency in statistical analysis, machine learning, and
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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science; quantum non-equilibrium processes; Casimir physics , Non-equilibrium quantum physics , Physics-informed machine learning , Quantum chaos and thermalization , Quantum Computation , Quantum
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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of the researchers of the DKM group are also affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) . The candidate is expected to join Integreat and strengthen the interdisciplinary
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learning, computational biology, and AI for science The postdoc will work at the interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and
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, software and data engineering, data mining, machine learning, and Artificial Intelligence. Qualified candidates are invited to submit their applications through the web portal available at https
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due