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The Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), Odense, invites applications for a fully funded PhD position in the foundations of programming
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The newly established Novo Nordisk Challenge Center for RNA Therapeutics Towards Metabolic Disease (RNA-META) at the interdisciplinary Nanoscience Centre , University of Aarhus, Denmark, invites
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Two postdoc positions on the ecology and physiology of microbial key players exposed to oxygen de...
with experience in one of the following directions: Analysis of meta-omics data sets, microbial physiology and cultivation, enzyme biochemistry or activity-based microscopy techniques (e.g. BONCAT
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publication in peer-reviewed scientific journals Research experience in some of the areas of large-scale greenhouse gas budget quantification, global meta-analysis. Further, we will prefer candidates with some
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are looking for a candidate to work with investigation of microbial diversity, nitrogen/carbon metabolism, and phototroph dynamics in marine oxygen minimum zones using multi-omics approaches (metagenomics, meta
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2025, or as soon as possible thereafter. The project explores two parallel technological routes for converting captured carbon dioxide into microbial biomass: One fully biological, and the other a hybrid
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Program . Job description The fellowships are aimed at early-career researchers with a basic science background or clinicians who aspire to a career in academia. We are particularly interested in candidates
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will be part of the section of Artificial Intelligence, Cybersecurity, and Programming Languages (ACP), an ambitious group that fosters collaboration, research excellence, and quality education. The
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the Challenge Programme of the Novo Nordisk Foundation: “Mathematical Modelling for Microbial Community Induced Metabolic Diseases”, led by Prof. Daniel Merkle. The expected starting date is 1 Januar 2026, with
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aspects of cheminformatics. The position is founded by the Challenge Programme of the Novo Nordisk Foundation: “Mathematical Modelling for Microbial Community Induced Metabolic Diseases ”, led by Prof