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and Python are required. The successful candidate will be based in Odense, under the primary supervision of Prof. Stefan Jänicke. The appointment will be made for 2 (two) years at a competitive salary
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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: Conceptualisation and theoretical development of spatiotemporal optimisation of biodiversity dynamics during recovery. Synthesising and modelling and species pools and biodiversity patterns given different
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to quantify the uncertainty in model outputs using different methods. Run scenario analysis to identify management practices with the largest mitigation potential, both spatially and temporally Support training
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and conducting laboratory work. Insight into applied mathematics, linear algebra, process-based modeling, and soil health indicators. Experience with Python, applied statistics, and gradient-based
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: Extensive experience in programming using Python, R, or other languages Research experience in remote sensing of cover crop, crop type classification, and crop aboveground biomass quantification Insight
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knowledge of C++, Python, and R. Who we are At the Department of Agroecology, our main goal is to contribute to sustainable solutions to some of the world’s biggest problems within the areas of soil, plants
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to evaluate the spatial and temporal patterns of how those components interact. And the main focus of your position will be to evaluate how different agroecological practices contribute to soil health
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Post Doctoral Researcher in Human-centred Large Language Models for Software Engineering, Departm...
Python. Be the main author in at least one journal publication in the area of AI4SE, published at a high impact journal. Be the main author in at least two journal publications published at a high impact