91 data-"https:" "https:" "https:" "https:" "https:" "University of Minho" research jobs at Aarhus University
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
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a focus on metabolomic and transcriptomic data. Performing tissue sectioning, matrix coating of the resultant sections, data acquisition using a Bruker TimsTOF MALDI-2 instrument and downstream
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including practical experience of GLP implementation (extending to electronic record-keeping, and auditable processes for data acquisition, analysis and curation). Please ensure you read the Job Description
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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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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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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