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on the development of AI models for analysis of cardiac CT scans, with the aim to explore how machine learning models can quantify cardiovascular disease and predict future events from CT scans. The project will
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Institutions. According to the Ministerial Order, the progress of the PhD student shall be assessed at regular points in time. Who we are The AI4OR group operates at the intersection of AI / machine learning and
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Professor (tenure-track) and/or Associate Professor levels with a focus on Data Science and Machine Learning. The expected starting date is summer 2026, with room for flexibility. Depending on the profile
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++, Phyton, and machine learning is necessary as well as excellent communication skills in English. Applicants with experience in digital energy, and if possible, co-simulation frameworks such as FMI/FMU, will
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well as experience in automated fabrication and mechanical characterization. A solid background in modelling and system identification is essential, with particular emphasis on data-driven and machine-learning–based
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expect the candidate to have: PhD in transportation science, machine learning, behavioral economics or a related field. Programming skills Python, along with experience working with transportation
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of machine learning and earth observation within the general study
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning for improving
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or interest in runtime reconfiguration techniques and system safety considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an
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of the following areas: Robotics Control theory Deep Learning & Machine learning Modelling and control of soft/continuum robots Experience with embedded systems programming (Preferably) with a strong background in