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Description The Department of Mathematics (www.math.ethz.ch) at ETH Zurich invites applications for the above-mentioned position at the Seminar for Statistics (www.math.ethz.ch/sfs). Candidates should
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using multivariate statistical methods, results that will be compared with other, independent temperature reconstructions, high-resolution palaeobotanical records, and vegetation modelling scenarios
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first-in-human through proof-of-concept studies. As trusted partners in early development, we design efficient and innovative clinical trials, apply rigorous statistical methods, and implement high
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models, and unsupervised learning to identify high-order structure in neural and molecular data. • Conduct statistical modeling of temporal trajectories and population dynamics across thousands of neurons
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sequencing data, including both pre– and post–base-calling analysis, to support the development of a next-generation diagnostic platform You will design algorithms and statistical models for real-time sequence
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, signal in-tegrity) under varying physiological and environmental conditions Conduct statistical evaluation and model validation using controlled measurements and sensing-dummy reference data Collaborate
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Communication: Performing statistical analyses and modeling to translate findings into actionable management recommendations for water abstractions The full-time position is funded for four years. Salary and
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midge) larvae. Based on these sensitive temperature indicators, they will develop quantitative reconstructions of glacial temperatures using multivariate statistical methods, results that will be compared
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at national and international conferences Profile A MSc degree in hydrology, statistics, data science, applied mathematics or a closely related field Extensive experience with setting up, fine tuning, and
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. Subsequent analysis of results requires GraphPad Prism, KinTek Explorer (kinetic analysis), Excel, and Statistics. Work in a multi-disciplinary environment collaborating with cell biologists and chemists