51 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions at Chalmers University of Technology in Sweden
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data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods
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international academic institutions and 14 industry partners (https://euraxess.ec.europa.eu/jobs/401249 ). We work together in the field of fluid-structure interaction in technical systems and industrial
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generative machine learning models to create an active learning cycle to identify materials with adequate properties. Promising materials will be synthesized, characterized and evaluated in lab. This will help
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, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our dedicated webpage . About the research project We will recruit a
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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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to the forefront of quantum technology, and to build a Swedish quantum computer. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and
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with catalysis/photochemistry Programming skills using Python and MATLAB Analysis of complex scientific data through machine learning What you will do Plan experiments together with your supervisor and
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will use advanced evaluation techniques, data mining, and generative machine learning models to create an active learning cycle to identify materials with adequate properties. Promising materials will be
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will (micro-)benchmark Java-based applications using JMH. You will collect performance measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning
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applications for a Doctoral student position in applied mathematics and machine learning for urban 3D reconstruction, within the Digital Twin Cities Centre (DTCC). The project aims to create analysis‑ready