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74 Max Perutz Labs Startdate: 24.11.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 23.11.2028 Reference no.: 4605 Explore and teach
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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for the Research Associate, Grade 7 level, position must have a PhD in a quantitative biology discipline, statistics or machine learning along with a proven track record of research using statistical modelling
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leadership of their own research programme. The ideal candidate will have the following qualifications and attributes: A PhD in mathematics, theoretical physics, or a closely related discipline (or expected
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of glucose. You will hold a PhD in a relevant field; A lower grade offer may be made (Grade 6) with comparable reduction in responsibilities and amendment in job title to Research Assistant, if a suitable
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subjects: computational biology or machine learning, statistical genetics and/or have relevant post-qualification experience in quantitative, genomic or molecular epidemiology, or statistical genetics. You
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years of experience as a Lab engineer, including the PhD. Job-SpecificSkills: Strong understanding of machine learning principles, particularly deep learning architectures (e.g., transformers, RNNs
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key