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and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
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hardware Experience with atomic layer deposition and process development Experience with thin film and materials characterization Strong background in computational materials science and machine learning
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning
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targets to treat anhedonia. Proposals that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects
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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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Experience in machine learning Knowledge of SDN and NFV Knowledge of basic TCP/IP protocols What you will do Conduct high-impact research and publish in leading journals and conferences Shape research
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development in large multi-site cohorts; and 3) developing advanced machine learning models of task-related brain function. The fellow will have the opportunity to contribute to these projects and will also be
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Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Switzerland | about 1 month ago
(e.g. R or Python), statistics, machine learning, and data science. A good publication record with respect to your career stage and research interests in climate impacts in mountain regions complete your
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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