105 associate-professor-computer-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Aarhus University
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The Department of Clinical Medicine, Faculty of Health, Aarhus University, invites applications for a position as Clinical Professor in Oncology, to commence on 1 May 2026 or as soon as possible
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-biologists. The team consists of a dynamic mix of experimental and computational biologists aiming to examine the factors and complexes governing the production and turnover of eukaryotic transcriptomes. What
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. You will report to the Associate Professor Martin Kristian Thomsen. Your competences You have academic qualifications at PhD level, for example within the following areas of molecular biology or
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friendly atmosphere of large and interacting communities for e.g. structural biology, molecular cell biology, computational biology, neurobiology, and molecular medicine that encourages lively, open, and
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Contribute to and author publications. You will report to Markus Rinschen, Associate Professor at the Department. Your competences You have a background within biology, medicine, (bio)chemistry or
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staff of 43 full, associate and assistant professors, a support staff of ~40 technical and administrative staff, ~150 PhD-students and ~100 postdocs and around 350 students. In addition to excellence in
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computing (HPC), and AI are strategic focus areas. At the department, you will be part of a technical team with other academic technicians, who work with large amounts of data, both from computational and
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Aarhus University with related departments. Contact information Before applying or for further information, please contact: Associate Professor Aurelien Dantan, +4523987386, dantan@phys.au.dk . Deadline
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can be obtained from Associate Professor Sheng Wang (e-mail: swan@agro.au.dk ; Phone +45-9350 8301), Associate Professor René Gislum (e-mail: rg@agro.au.dk ; Phone: +45-87158279), and Center Director
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater