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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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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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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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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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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
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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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the ERC Starting Grant research project “Exploiting Nanopore sequencing to discover what microbes eat (NanoEat)” with the aim to combine state-of-the-art metagenome sequencing with state-of-the-art data
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to support decision-making by integrating physical models and sensor data. These methods are validated through industrial case studies, with a particular emphasis on critical infrastructures where complexity
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to vastly improve our ability to recover microbial genomes from metagenomes. Specifically, we aim to take advantage of the vast amount of high-quality long-read data that is readily obtainable using