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-time data into WRF-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus
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species distribution models, joint/hierarchical community models, dynamic vegetation models, or related biodiversity forecasting approaches; advanced programming skills in R and/or Python and
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level, which is in Danish). Scientific computing, possibly with different programming languages (e.g., Python, MATLAB/Simulink). Moreover, the candidate shall demonstrate the following experiences
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languages (e.g., Python, Julia, C#, C++ MATLAB). Parametric and algorithmic building design using, e.g., Rhino/Grasshopper tool suite. Physics-informed machine learning, generative and agentic AI for
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The Center for Sustainable Energy Materials (CENSEMAT) at the Department of Chemistry, Aarhus University, invites applications for a 24-month postdoctoral position in computational materials
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research results as well as actionable insights. Candidates may come from cognitive science, psychology, computer science, computational social science, consumer research, behavioural economics, complexity
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The Center for Quantitative Genetics and Genomics (QGG) at Aarhus University, Denmark, invites applications for a two-year postdoctoral position in plant regulatory genomics and artificial
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institution, industry, start ups or similar. You have a clear vision for how research-based innovation generates impact from natural science research, and experience with translating research into innovative
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supervised by Professor Cecilia Høst Ramlau-Hansen (Department of Public Health), MD, PhD Nis Brix (Department of Clinical Genetics), and Professor Doug Speed (Center for Quantitative Genetics and Genomics
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. Metagenomics and/or metabarcoding. Bioinformatics and analysis of high-throughput sequencing data. Statistical analysis using R and/or Python. Reproducible computational workflows. Scientific writing and