36 data-"https:"-"https:"-"https:"-"https:"-"J.-F" Postdoctoral positions at Aarhus University
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lead the image processing and computational analysis efforts, developing robust methods to register, segment, and analyse spectral micro-CT data, and — where relevant — advance reconstruction and
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research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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Electrophysiological characterization of muscle fiber excitability (in collaboration with the research group) In vivo studies using animal models of neuromuscular disease Integration of molecular and transcriptomic data
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environment. There is overall a close collaboration between the academic and the clinical staff at DCPT. About the research project The research project of this postdoc will be based on follow-up data from a
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include psychiatric disorders as well as clinical and social outcomes, but specific tasks may depend on applicants. The positions will generally involve various data analyses using Danish register data and
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for the position in question is a broad ranging of techniques ranging from spatial and single-cell analyses to classic methods like histology cell culture and Western blotting. Data handling through bioinformatics
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accompanying families, including assistance with relocation and career counselling to expat partners. Please find more information about the International Staff Office and the range of services here . Aarhus
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Development for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria Fluency in English Strong skills in
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to Postdocs, Research Assistants, Research & Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will