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molecular docking, molecular dynamics and free-energy methods (MD/FEP), machine learning for molecular design, and protein–ligand modelling. Experience bridging computational and experimental groups, and the
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Job Description Are you an established researcher in probabilistic machine learning, with a passion for developing robust, trustworthy, and explainable AI methods for applications in science and
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motivated candidate with a strong background in statistics and/or machine learning. Areas of particular interest include, but are not limited to: Causal Discovery and Causal Inference Extreme Value Theory
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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At the Technical Faculty of IT and Design of the Department of Sustainability and Planning, Copenhagen, a position as Postdoctoral researcher in Geospatial Machine Learning for Predicting Land Use
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provide the possibility for the student to work with LLMs and machine learning. Your competencies Interest in learner centered technology design, in particular how AI systems can scaffold reflection, agency
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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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 Assistance in Complex Acoustic
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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to be 10 million by the year 2050 if new approaches are not undertaken. By joining forces in artificial intelligence (AI), machine learning (ML), chemistry and molecular biology, we intend to develop new