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Theory for Manifold Data and Bayesian Smeariness of Location Statistics”. The project will explore the effects of non-Euclidean geometry on Bayesian inference from a frequentist Bayesian perspective
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an interplay between approaches in statistics, geometry and optimization. We offer an interdisciplinary qualification program that equips our graduate students with a broad set of skills for analyzing data and
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and ideally have very good knowledge in statistical physics, and, as a plus, knowledge in electrodynamics, non-equilibrium physics, quantum field theory, condensed matter theory or numerical methods
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-Markovian baths. You have completed your scientific university studies with a master’s degree in the field of physics, with above-average grades and ideally have very good knowledge in statistical physics
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or equivalent. initial experience with finite element modeling, time series analysis, Bayesian statistics interest in methodological development work, careful independent supervision of experiments The University
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social sciences disciplines, strong analytical skills, experience with relevant statistical methods, proven interest in topics related to sustainable food systems, strong computer skills, and excellent
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Knowledge of empirical methods and relevant statistical software (e.g., Stata, R) Advanced knowledge of English (C1) and Spanish (C1); other languages will be an asset Willingness to cooperate and ability
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software, ability and willingness to perform statistical data evaluations and initial experience in project management. Your application should include: a letter of motivation outlining your personal
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Knowledge of quality assurance procedures in statistical programming Knowledge of the Carpentries initiative Involvement in open source / open science communities We offer you interdisciplinary and exciting
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applied statistics explore the potential of mixed forest stands for supporting multifunctional silvicultural approaches. We focus on European temperate forests dominated by European beech and try to uncover