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the foundations of computer science. Subject Description The research subject focuses on an integrated development of network architectures, resource efficient algorithms, and programming paradigms for enabling
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experience in experimental particle physics and data analysis Prior experience with machine learning tools Prior experience in developing algorithms such as particle identification, specific final state event
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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funded by a EU programme Reference Number 304--1-14162 Is the Job related to staff position within a Research Infrastructure? No Offer Description Join a research team developing state-of-the-art open
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the mathematical foundations of these fields, e.g., designing innovative algorithms and control strategies, as well as the development of technical solutions to adapt these new methods to applications in the areas
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and synchrotron X-ray microtomography; experience with 4D (in-situ/operando) X-ray microtomography experiments; experience in developing 3D/4D image data analysis algorithms; experience using 3D/4D
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, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation group. Main responsibilities Conduct research in collaboration with senior researchers and
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Prior experience with machine learning tools Prior experience in developing algorithms such as particle identification, specific final state event selection, or object reconstruction Interest in student
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both academic research and industrial applications. In addition to theoretical research, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation
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embodied cognitive agents can learn to solve complex problems and adapt in dynamic real-world environments. This research direction demands developing novel techniques and algorithms that can enable