495 data-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions in Denmark
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comprehensive training in translational liver research, including data analysis, experimental design, and scientific dissemination. Qualifications We are looking for a highly motivated PhD candidate with a strong
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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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to the faculty’s departments. Consequently, your employment will as of that date be with a department. Contact information For further information, please contact: Professor Troels Skrydstrup, +45 28 99 21 32, ts
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paths at DTU here . Further information Further information may be obtained from Professor Julia Kirch Kirkegaard (jukk@dtu.dk ) and/or Section Head Tanja Schneider (tansch@dtu.dk ). You can read more
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authorities, and the other DeiC departments working with quantum computing and data management. We help facilitate Danish researchers’ access to the LUMI supercomputer, which ranks in the Top 10 fastest in
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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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processing historical data, and the tasks will be relevant to the your further course of study. We are looking for a student who: Has strong quantitative skills Is proficient in Excel, Stata, and either R
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Job Description Are you passionate about environmental contaminants, food safety, marine ecosystems, and creating real-world impact through cutting-edge analytical and data-driven approaches? Do you
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will find contact persons at the bottom of the jobpost. Further information Read more about our recruitment process here The appointment process at Aalborg University involves a shortlisting process. You
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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