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Field
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, either with R or GIS programmes or equivalent Excellent writing skills Excellent written and spoken communication skills in English The ability to work independently while contributing to a collaborative
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and analyzing ecological or environmental data Experience with data handling and analysis in R Experience in working with spatial datasets, either with R or GIS programmes or equivalent Excellent
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agent systems to let biologists query, retrieve, and reason over large-scale image databases We are also open to your ideas that align well with our interests and expertise! About us: Our mission is to
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of reinforcement learning agents understandable to humans, leading to improved transparency, trust, safety, and regulatory compliance in high-stakes decision-making systems. Potential application domains include
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks, environments, or objectives while retaining
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; Interest in interdisciplinary environmental assessment and offshore renewable energy systems; Experience with programming and/or data analysis (e.g. Python, R, MATLAB, GIS); Excellent written and spoken
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Fusarium graminearum, the causal agent of Fusarium Head Blight (FHB), and the English grain aphid (Sitobion avenae), one of the most economically important pests of cereal crops. FHB causes substantial
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, R, MATLAB, GIS); Excellent written and spoken English skills; Ability to work both independently and collaboratively in an interdisciplinary research team. What we offer Our goal is to work together
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energy/water balance, evapotranspiration, thermal comfort, heat-health interactions. Solid programming and data analysis skills (e.g. Python, R, GIS). Willingness to travel for validation-related fieldwork