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the competences of the IML group within prediction of T cell immunogenicity and immunoinformatics in general. You will work with nearby bioinformatics, postdocs and PhD students working on other projects within
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. This position is part of the projects PROCESSOR – Phosphorus recycling from complex scarcely soluble societal resources – letting the soil do the work, a collaborative research project. CRUCIAL - Closing
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processes, their regulation and their significance for cell function, interaction and organisation in tissues. The Department offers comprehensive undergraduate and graduate teaching programs spanning cell
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to modification. The overarching goal is to provide fundamental insights into the molecular-level changes that occur during protein modification. The postdoc will work in a group focusing on protein modification
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process, and subsequently implement this infrastructure on top of an existing cloud infrastructure. You will play a key role in the project's development, ensuring technical tasks and teams work together to
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cross-institutional collaboration and knowledge exchange. This project offers a unique opportunity to work at the intersection of academia, start-ups, and large-scale industry partners. Our focus is on
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, scipy, scikit-learn, pytorch, pytorch geometric, etc.). Proficiency in statistics and graph machine learning, including the ability to build and deploy models, and evaluate their performance. Software
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the effectiveness of the digital nudging approaches and publishing the findings in high-impact venues. Moreover, the postdoc will actively support the management of SDU-led work packages, including coordinating
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responsible for developing and implementing hazard and health/safety (SSbD Steps 1–3) for biobased materials used in multiproduct biorefineries. The SSbD work supports substitution of harmful substances, safe
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in more than 200 genes. Most of these genes encode proteins that assemble the TZ, an evolutionary conserved multi-molecular superstructure that lies at the base of cilia and allows selective diffusion