86 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" research jobs at Aarhus University in Denmark
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. Consequently, your employment will as of that date be with a department. Contact information For further information, please contact: Assistant Professor Emil Laust Kristoffersen, +45 29271306, emillk
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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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analysis) Data collection, documentation, and basic data analysis Contribution to reporting, presentations, and potentially scientific publications Supporting collaboration within the research group and with
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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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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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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
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to diversity and inclusion, and success in mentoring. Place of work The place of work is the Department of Food Science, Aarhus University, Agro Food Park 48, 8200 Aarhus N, Denmark. Contact information
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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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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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graph algorithms for optimization under physical constraints Applying graph mining and graph data management techniques Designing computational methods for waste heat reuse and green transition goals