99 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Aarhus University
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: https://ece.au.dk What we offer The Department of Electrical and Computer Engineering offers: An exciting opportunity to work on cutting-edge research in IoT systems and critical infrastructure monitoring
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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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and Teaching, is available at: https://dpu.au.dk/en/research Teaching The selected applicant is expected to have solid experience of teaching at university level and should be prepared to teach and
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background. Job description The successful applicant is expected to contribute significantly to the department’s research and teaching environment. You are expected to teach and supervise students at BA, MA
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fishing activities, major shipping routes, and offshore development locations. The EU Oceans Pact highlight the need to assess and manage dumped munitions. Two EU-funded projects, MUNI-RISK ( https://muni
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details, see the ACE website: http://www.econ.au.dk/ace ACE is funded by the Danish National Research Foundation with a Center of Excellence grant for the period 2025-2031, and includes researchers from
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and applied mathematics and offers a dynamic and collegial academic atmosphere. More information about the department can be found at https://math.au.dk. Place of Work and Area of Employment The place
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see: http://ecos.au.dk/en/ . What we offer The department offers: A multi-disciplinary research environment collaboration within strong research teams with extensive experience in carbon flux research
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to new projects. Assist with troubleshooting their machines and support their understanding of core concepts. Guide students in working on their own project. Demonstrate best practices and foster
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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