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50 Faculty of Life Sciences Startdate: 01.10.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 17.11.2025 Reference no.: 4674 Explore and teach
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laboratory, including the development of SOPs and BIOCOSHH forms The Person Knowledge, Skills and Experience Ability to work well as part of a team and rapidly acquire new skills Detailed knowledge of innate
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researchers in applied mathematics and machine learning. This is due to its remarkable flexibility, mathematical elegance, and as it has produced state-of-the-art results in many applications. As a leading
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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successful in this role, we are looking for candidates to have the following skills & experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation
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backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute
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on evaluating the abilities of large language models (LLMs) of replicating results from the arXiv.org repository across computational sciences and engineering. You should have a PhD/DPhil (or be near completion
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-quality academic articles and publish them in internationally recognised, reputable journals. You will mentor and co-supervise PhD students affiliated with the project. You will assist with project-related
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no.: 4520 Among the many reasons to research and teach at the University of Vienna there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves as
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computational biology and mathematical spatial analysis via independent study and training courses. It is essential that you hold a PhD/DPhil (or close to completion) in mathematics, computational biology, data