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individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics
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mode proteomics to enhance bacterial proteome coverage. This project aims to improve the detection of acidic proteins, contributing to a better understanding of bacterial biology and unlocking new
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conferences You will report to the Markus Rinschen, MD, Associate Professor. Your competences You have a background within Biomedicine and experience with molecular biology, bioinformatics, or other biomedical
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conferences You will report to the Markus Rinschen, MD, Associate Professor. Your competences You have a background within Biomedicine and experience with molecular biology, bioinformatics, or other biomedical
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. Master’s degree in bioinformatics, computer science, data science or a closely related field Relevant ‘life science’ interest and coursework in e.g. molecular biology/genetics/microbiology Experience with
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Two funded PhD positions at SDU Physics - Interdisciplinary research projects within experimental...
research. We are happy to move beyond traditional disciplines and work to develop new technologies and sciences across physics, chemistry, pharmacy, mathematics, computer science and biology. The
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two-year master's degree. Master’s degree in bioinformatics, computer science, data science or a closely related field Relevant ‘life science’ interest and coursework in e.g. molecular biology/genetics
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collaborators on studies and strategies Assist with documentation and communication of obtained results. You must hold a MSc degree (or equivalent) ideally in biological science, microbiology, cellular biology
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interconnected challenges and conditions a) TCR diversity, b) limited quality and quantity of data biased towards a few well-studied epitopes and c) lack of Deep Learning (DL) tools tailored specifically
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postulate, is due to three interconnected challenges and conditions a) TCR diversity, b) limited quality and quantity of data biased towards a few well-studied epitopes and c) lack of Deep Learning (DL) tools