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tools from chemistry and biology, and apply these in studies of therapeutic peptides and proteins. Our aims are to develop modulators for protein-protein interactions (PPIs) and to provide molecular-level
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spend some time on placements in the industrial partner. We are seeking ambitious, inquisitive and innovative individuals to join our multidisciplinary research team. As part of a dynamic and
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The aim of this project is to describe ion conduction and activation/inactivation processes by employing molecular dynamics and statistical mechanical methods. The expected outcome is an improved
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and biotic stresses. We utilize state-of-the-art LC-MS and RNA-seq techniques to address significant questions in tRNA biology, for example how post-transcriptional tRNA modification dynamics influences
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of molecular data in cancer genomics. The position is connected to the project “Bayesian Rank-based unsupervised Integration of multi-source Data in cancer Genomics and the digital Economy (BRIDGE)”, recently
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of computational chemistry. Applicants can have a background from cheminformatics including RDKit, machine learning applied to chemistry, and molecular modeling Our group and research- and what do we offer? Our
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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, your application should include (PDF format): A letter of motivation (max. 1 page) A detailed curriculum vitae. Please include an overview of your experience with molecular dynamics, density-functional
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drug development to inhibit or deregulate disaggregase activities. We want to dissect the molecular basis of ClpG and ClpL activity control. How is the activity of the disaggregases repressed in absence
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equipment e.g. STM. Simulating fabrication methods. Collaboration with other groups at NQCP and companies/academic groups in and around the Copenhagen area. Join us in this major confluence of exciting