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available in the further tabs (e.g. “Application requirements”). Programme Description Applicants holding a PhD or who are eligible to be a junior professor in the historical humanities are invited to apply
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are described here . Research Environment The University of Konstanz has been successful in the German Excellence Initiative and its follow-up programme, the Excellence Strategy and is among the most highly
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Muehlleitner (ITP), Prof. Dr. Thomas Schwetz-Mangold (IAP), and Prof. Dr. Felix Kahlhoefer (TTP). KIT is also home to a lively experimental program in astroparticle physics (https://www.iap.kit.edu/english
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) Application Deadline 30 Jan 2026 - 12:37 (UTC) Country Germany Type of Contract To be defined Job Status Other Offer Starting Date 5 Jan 2026 Is the job funded through the EU Research Framework Programme? Not
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of Livestock Systems is newly established at TUM and will include several postdocs, PhD students, and technical staff. The team will work on various topics, including multi-scale analyses of productivity
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analyzing developmental-oncogenic signaling pathways using a combination of experimental and computational approaches. Within this project, we are creating perturbation-based genetic maps at the level of
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endolysins from chloroplasts. In addition, the position involves the implementation of bioinformatic analyses for the identification of natural endolysins as well as the computational development and
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. Applicants should have a background in population genetics/genomics, molecular ecology, biodiversity informatics, or a related field. Experience with large-scale data analysis is essential. Additional
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qualifications: University degree (PhD, M.D., or equivalent) in biology, life sciences, or biomedicine Strong motivation and a genuine interest in stem cell biology, hematopoiesis, and immunobiology At least basic
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adaptive systems that jointly optimize morphology and control for real-world physical interaction. Requirements Ph.D. in Computer Science or a related discipline. Strong background in Machine Learning