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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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receive training and skills in some of the following: meta-barcoding, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous
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. Desirable Familiarity with supply chain management, operations, or organizational contexts. Experience with advanced statistical methods (e.g. multilevel modelling, causal inference, Bayesian methods
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analysis, Bayesian Skyline Plots, PCA, Bayescan - information provided in the CV and/or in the motivation letter; Other professional experience: teaching activities in evolutionary biology and phylogenetics
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experience (front-/back-end, metrology/inspection, equipment maintenance, yield-improvement projects). Digital twin, IIoT, MLOps, real-time data streaming, edge computing. Causal/XAI, Bayesian methods
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, machine learning, deep (reinforcement) learning, Bayesian optimisation, control theory, dynamical system theory and/or robotics. Experience with hardware development is desirable but not mandatory. You have
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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine