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50 Faculty of Life Sciences Startdate: 01.05.2025 | Working hours: 20 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 30.04.2031 Reference no.: 3736 Explore and teach
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learning Prior experience in deep learning, or/and
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informed neural networks (PINN) and explainable machine learning (EML) frameworks; experience in related technologies including large-scale data analysis, deep learning, Python, PyTorch; and the ability
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both. Ideally, you bring strong technical expertise and the curiosity to work across modalities and domains. Your responsibilities Design and implement machine learning and deep learning pipelines
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learningPrior experience in deep learning, or/and
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industrial stakeholders, and we have ongoing collaborations with Fujifilm Diosynth, Opentrons, Lonza and Neochromsome. In collaboration with OccamBio Ltd, we aim at designing deep learning models to engineer
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record (EHR) as well as MyChart data, with the opportunity to work on applications of machine learning/deep learning/ Natural Language Processing in novel areas of healthcare. The position is open for a
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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science, and applied plant research Example reading: Peleke, F. F., Zumkeller, S. M., Gültas, M., Schmitt, A., & Szymański, J. (2024). Deep learning the cis-regulatory code for gene expression in selected
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one of the following domains are highly desirable: deep learning models on natural language processing or computer vision, advanced analysis of fMRI using encoding or decoding models, computational