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, Physics, Computer Science, etc.), a strong background in Data Science, Statistics, or Machine Learning, and an interest in Biomedicine. Alternatively, a candidate with a biomedical background with some
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for modeling and prediction. Our research is based on statistical machine learning and signal processing, on quantitative analysis of digital media and text, on mobility and complex networks, and on cognitive
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these signals offer for modeling and prediction. Our research is based on statistical machine learning and signal processing, on quantitative analysis of digital media and text, on mobility and complex networks
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Topology, Probability Theory as well as in Statistics, Data Science and Operations Research. A successful candidate should have demonstrated the ability, or have the clear potential, to: Participate in and
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, and managers) Experience in data analysis and statistical tools Preferable experience with advisory work Who we are The Department of Ecoscience is engaged in research programs and advisory work
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, stakeholders, and managers) Experience in data analysis and statistical tools Preferable experience with advisory work Who we are The Department of Ecoscience is engaged in research programs and advisory work
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.) for optimal operation using e.g. model predictive control. You will use stochastic and statistical modelling concepts together with domain-knowledge to develop such models. Afterwards, the models are used in
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background in statistics and behavioural economics. Your primary tasks will be to: Design workflows for quantitative societal sustainability assessment of low Technology Readiness Level (TRL) biotechnology
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languages (eg, R, Python), implement statistical associative models (eg, GLMM), as well as experienced in simulation development (eg, multi-agent based models). You will also work with stakeholder engagement
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the behaviour of fishes, particularly in the wild. Analytical skills and experience with statistical software (e.g. R, MatLab, Python) are expected as is experience in planning and executing fieldwork. Experience