285 data "https:" "https:" "https:" "https:" "Dr" "UCL" "UCL" Postdoctoral positions at Nature Careers
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mission is to transform bold scientific ideas into advances that improve human health. Within this mission, the Health Data Science Centre (HDSC) at HT has been planned in partnership with Politecnico di
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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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for the analysis of ONT data are expected. The successful applicant will work in the team of the Joint Microbiome Facility of the Medical Univeristy of Vienna and the Univeristy of Vienna (JMF), and support Centre
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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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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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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postdoctoral researcher, you will play a central intellectual role in shaping experiments, generating and analyzing high-dimensional data, and integrating single-cell insights with complementary datasets
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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