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Field
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unique data collection opportunities, including large-scale agricultural household surveys, measurement experiments (such as DNA fingerprinting for plant variety identification), and impact studies
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Max Planck Institute for Biological Intelligence (Seewiesen site), Seewiesen | Germany | 2 months ago
. The Avian Sleep group aims to understand how birds reconcile the need for sleep with ecological demands that seemingly require wakefulness. Using EEG data loggers in the wild, we demonstrated that birds can
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of quantitative analytical methods to large datasets. Study key trends and gaps in agricultural sustainability in Southeast Asia. Coordinate and organize workshops with international collaborators. Presentation
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biology, biophysics, computer science, engineering, mathematical physics, or related disciplines. Reporting to Research Scientists, Data Scientists or the Center Director, as appropriate, Fellows
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and research protocols in compliance-focused environments. Advanced computer skills with experience using Microsoft Word, Excel and PowerPoint; specific experience in working with a range of analytical
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biomolecules. Proficiency in programming or scripting for simulation and data analysis, along with experience using high-performance computing environments, is required, and experience using structure prediction
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processes, our work leverages big data and systems biology approaches to uncover novel insights into human health and performance. This role offers an exciting opportunity to grow your research profile while
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. · Monitor fellow supervision at all participating sites. · Coordinate educational activities for rotating fellows from outside institutions. · Prepare and submit all information required and
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interactions across a range of physical platforms to build large-scale, hybrid quantum networks. Our work spans rare-earth-ion-doped solids, alkali vapours, quantum dots, and nonlinear photonic media, aiming
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of analyzing large-scale population data. Experiences working with electronic health records (desirable). Understanding of clinical informatics approaches (e.g., machine learning, Bayesian statistics) and