52 machine-learning "https:" "https:" "https:" "https:" "https:" positions at Chalmers University of Technology in Sweden
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machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome
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at the Division of Data Science and AI at the Department of Computer Science and Engineering . Join our innovative team and contribute to exciting research in theory of machine learning, in a collaborative and
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-energy devices. Using state-of-the-art electronic-structure calculations and machine learning methods, you will model these effects and contribute to the design of improved semiconductors for solar cells
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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction
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computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience