66 data "https:" "https:" "https:" "https:" "ETH Zürich" Postdoctoral positions at Nature Careers in Denmark
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proliferation and the faithful transmission of genetic information to daughter cells. However, replication forks are constantly challenged by a wide range of intrinsic and extrinsic stressors, including metabolic
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. The Section for Wildlife Ecology is situated in Aarhus and employs 35 staff members, including six affiliated with the bat research group. For more information on the Department see: http://ecos.au.dk/en/ What
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. Specifically, the project will combine 30 years of Danish health data at the service of hundreds of women with endometriosis, recruited through online platforms. It will use AI-enhanced methods to handle
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project activities. Key responsibilities include: Investigation into the GEUS sediment archive to extract information on the properties of subglacial sediments deposited by past ice streams. Analysis
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structures. You will work closely with computational researchers to gather data, evaluate AI predictions, and design experiments. You will work in a team with 7 PhD-students and 4 postdoctoral researchers and
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, and train deep learning models on the resulting data to design new antibiotic compounds that evade both current and likely future resistance mechanisms. Your computational work will directly steer
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, close working relations, network activities among young scientists, and social activities a workplace characterized by professionalism, equality, and a healthy work-life balance Contact Information
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University with related departments. Contact information For further information, please contact Prof Kim Daasbjerg at +45 23 48 52 49 or kdaa@chem.au.dk or alternatively Associate Professor Behzad Partoon
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information For further information, please contact: Professor Torben Heick Jensen, thj@mbg.au.dk, phone +45 60202705 Application procedure Shortlisting is used. This means that after the deadline
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum