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clinical partnerships. You will gain experience of research coordination, intensive patient and public involvement work, and strategic network building. You will contribute to the REPhRAME team’s
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Job Description The postdoctoral researcher will conduct independent and collaborative research on projects involving brain network analysis, including investigations on the Drosophila connectome
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networks, from a machine learning and information theory perspective. This basic research project has strong translational potential and aims to elucidate how immune function is altered during sepsis, with
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using advanced seismological and geophysical methods, including surface-wave tomography, receiver functions, reflected seismic phases, joint inversion, and Distributed Acoustic Sensing. The main objective
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methods (e.g., variational autoencoders) applied to biomolecular dynamics data. ● Familiarity with elastic network modeling (ANM/GNM) or the ProDy software package. ● A track record of independent
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performance in intelligent networked systems. The appointee is expected to make significant contributions to the research agenda of the SHIELD project. SHIELD investigates advanced methods for improving
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distributed decision-making. Applicants must have a PhD in Electrical Engineering, Mechanical Engineering, Computer Engineering, Applied Mathematics, Mathematics, or a closely related discipline, and are within
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and in industry. We value the qualities that an even distribution of age and gender, as well as ethnic and cultural diversity, can contribute to the organisation. Application The application is to be
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Deutsches Zentrum für Neurodegenerative Erkrankungen | Magdeburg, Sachsen Anhalt | Germany | 14 days ago
programming skills (R, Python and/or MATLAB) and familiarity with multivariate analysis methods (e.g. RSA) Good communication skills and ability to work both within a local and a distributed
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. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious disease dynamics, uncertainty quantification