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Exoplanet Group at DTU Space. At DTU Space, you will have access to data from world-class telescopes, including JWST, ultra-precise RV spectrographs (e.g., HARPS-N and EXPRES), and access to ESO facilities
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analytical methods to large data sets The possibility for contract extension Flexibility in planning working hours Advanced professional training opportunities Possibility of participation in international
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these new large-scale data sources to develop new approaches for analysis of microbial horizontal gene transfer using long-read metagenomics in complex environments. The postdoc will be part of the Microbial
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of cellular aging, resilience, and fibrosis. Responsibilities Develop and implement analytical pipelines for large-scale single-cell, spatial, and multi-omics data integration Build and apply machine learning
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and applying genetic and genomic approaches to biodiversity research. This includes integrating environmental DNA (eDNA) and molecular tools with ecological data to enhance our ability to assess
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sensing and responding to the chemical landscape surrounding them as well as the chemical signals inside of them. This project is devoted to gather large data sets to investigate links in olfactory receptor
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Experience with writing scientific publications Experience in working with contaminants like PFAS, PCBs and mercury, fatty acids, stable isotopes and modelling in R A talent for working with large data sets
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laboratory imaging techniques on PV modules to large scale field inspections. You will contribute to the development of daylight electroluminescence and photoluminescence inspections together with data-driven
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Foundation RECRUIT grant ("Data Management, Algorithms, & Machine Learning for Emerging Problems in Large Networks – with Interdisciplinary Applications in Life & Health Sciences". NNF22OC0072415
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environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental sustainability. You will focus on processing