60 machine-learning "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" research jobs at Aarhus University
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, Belgium, and Germany, and offers the successful candidate excellent opportunities for interdisciplinary training, exchange, and scientific collaboration. Plant-PATH homepage: https://mbg.au.dk/plant-path
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the Ph.D. candidate is enrolled as a Ph.D. student at Aarhus University Graduate School of Health ( http://phd.health.au.dk/ ) in a separate procedure before starting as a Ph.D. student. The successful
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development for postdocs at AU. You can read more about it here: https://talent.au.dk/junior-researcher-development-programme/ If nothing else is noted, applications must be submitted in English. The
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engaged scientific environment at the Section for Arctic Ecosystem Ecology (for more information see: https://ecos.au.dk/en/researchconsultancy/research-areas/arctic-ecosystem-ecology ). The department is
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qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation models); Strong programming
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
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research is at the cutting edge of Human-Computer Interaction (HCI), personal fabrication, and physical user interfaces. As a research assistant, you will support our research team on implementing a novel
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/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning approaches, and development
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and