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Field
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to psychological and clinical measures and outcomes, epidemiology, statistics, and bioinformatics. Work in an environment of scientific excellence and interdisciplinary collaboration IMPRS-TP is a joint initiative
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bioinformatics and computational biology Strong interest in immune-brain interactions and food allergy-related immunology Excellent communication skills and fluency in English A collaborative mindset and
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bioinformatics and computational biology Strong interest in immune-brain interactions and food allergy-related immunology Excellent communication skills and fluency in English A collaborative mindset and
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biology, bioinformatics skills are welcome • Practical experience in molecular and cell biology, Western blot and FACS • Ability to work independently and in a team • Good English skills in speaking and
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professor, one assistant professor, one postdoc, and a large number of PhD students. The project includes strong partnerships with the University of Leipzig (Bioinformatics, Prof. Peter Stadler
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Experience with FACS, immune cell culture, molecular biology techniques and bioinformatics is advantageous Strong communication skills and fluency in English (spoken and written). Benefits: Fully funded PhD
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Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute • | Jena, Th ringen | Germany | about 15 hours ago
with interdisciplinary approaches, combining methods of microbiology, natural product chemistry, biotechnology, immunobiology, bioinformatics and systems biology. The ILRS faculty is composed
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Science is training researchers in functional genomics, quantitative proteomics, bioimaging, computational biology, and bioinformatics. Course organisation The IMPRS for Genome Science programme is embedded
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Scholarship Programme" (GSSP). Academic admission requirements We accept applications from students holding a Master's or comparable degree in bioinformatics, mathematics, physics, computer science
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experiments. Data & Analysis: • Collaborate with data scientists to analyze host and microbial data using statistical, bioinformatic, or machine learning approaches. • Contribute to the integration of spatial