79 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" research jobs at Nature Careers in Denmark
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cultural events including music festivals etc. See e.g. the recent recommendation by CNN (https://edition.cnn.com/travel/article/aarhus-denmark-things-to-do/index.html). Aarhus is easily reached through
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and technical-administrative staff and you have a flair for establishing collaborative relationships. Read more about the Department of Food Science at: https://food.au.dk/ The place of work is
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of publications (applicants applying for the position as senior researcher should indicate scientific highlights), H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching
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, 33621493, 33087936, 30566856, 39947938; doi: https://doi.org/10.1101/2025.03.15.641049 ). Postdoctoral Projects Project 1: Replisome Dynamics, Replication Stress, and Cancer Vulnerabilities This project aims
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of personal background. Apply online https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/jobs/preview/3538
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. The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background. Apply online https://fa-eosd-saasfaprod1
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Research Assistant in Physical Computing and Wearables at the Department of Computer Science, Aar...
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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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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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials