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fungal ecology, (meta-)genomics, bioinformatics, and multivariate statistics. Experiences in forest ecology and biogeochemistry are welcomed but not required. You are highly self-motivated and committed
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selection process with applications accepted year-round. Eligibility requirements: Candidates should hold a PhD degree in computer science, data science, mathematics, physics, statistics, or electrical
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sensing, and field sampling, and use these data to answer questions with statistical and process-based models. Project background We are inviting applicants for a postdoctoral position, starting as soon as
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, serving as a basis for statistical evaluations of stone formats, brick bonds, and the like. Where applicable, you also perform geometrical analysis of vault geometries and traces of formwork. You interact
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are required to have: A completed PhD degree. Experience in machine-learning methods. Some skill in at least one of these topics: Large data sets analysis Statistics and uncertainty analysis (probabilistic
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in RL Transfer learning in multi-task bandits and RL Statistical or generative decision making The exact project scope will be tailored to the candidate's strength and expertise. The candidate is