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50 Faculty of Life Sciences Startdate: 01.08.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.07.2029 Reference no.: 4160 Explore and teach
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algorithms; experience in 3D/4D (X-ray tomography) image processing; experience in machine-/deep-learning based image analysis; knowledge of tomographic reconstruction methods; experience in materials research
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: Automated tracking of ankle muscle fascicle kinematics in both superficial and deep muscles will allow for the intuitive and coordinated control of powered prostheses following leg amputations. In
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learning for dynamical systems, system identification and control systems. This can involve co-tutoring of PhD students or early R&D cooperative studies. contributing to the development of advanced benchmark
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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computational pipelines and deep learning of imaging. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications: No additional
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it is not required that the candidate possess a deep theoretical understanding of AI, practical proficiency in using AI will be considered in the selection process. Applicants must hold a PhD prior to
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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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, Mathematical Engineering, Mechanical Engineering or similar. Relevant skills: Strong background in machine learning/data science. Deep knowledge of neural network architectures (as a plus: PINNs, neural
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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