18 programming-"the"-"DAAD"-"UCL"-"U"-"SciLifeLab"-"IMPRS-ML"-"CSIRO"-"https:" "DIFFER" positions at Nature Careers in Denmark
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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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publications Strong programming skills in Python and deep learning frameworks such as PyTorch, demonstrated by coursework, projects, or contributions to public code repositories Experience with or strong
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Chips Act, and other similar programs. The position is open from March 2025, and the specific start date will be agreed with the successful candidates. What we offer A stimulating research environment
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different fields of research a work environment with close working relationships, networking and social activities a workplace characterised by professionalism, equality and a healthy work-life balance. Place
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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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Project Description We invite applications for a fully funded PhD position in experimental proteomics. The successful candidate will contribute to a fundamental research program exploring chemical
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engaged in research programs and advisory work covering the major biological sub-disciplines. We conduct innovative, advanced research in the areas of aquatic biology and ecology, Arctic environments and
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many national, international and industrial collaborators a research climate encouraging lively, open and critical discussion within and across different fields of research a work environment with close
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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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research project, we are seeking a highly motivated postdoc to work with high-throughout eDNA analyses from a range of different ecosystems and environmental sample types. The project will use powerful