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particle physics, perform data analysis and develop object reconstruction algorithms in the ATLAS experiment. You will become a member of the ATLAS collaboration and will be based in Nijmegen. Furthermore
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collaboration with Dr Flavia de Almeida Dias and a postdoctoral researcher in experimental particle physics. Your appointment will be for four years, starting in January 2026 or soon thereafter. It includes
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pioneering PhD project exploring how massive stars evolve into gravitational-wave sources. Combine stellar physics, simulations and cutting-edge data to uncover the origins of black holes and neutron stars
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postdoctoral researcher working in this project (see below), leveraging data from experiments in our project. Third, you will explore how local connection capacity markets and novel connection agreements can be
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these machine learning-based proxies together with a postdoctoral researcher working in this project (see below), leveraging data from experiments in our project. Third, you will explore how local connection
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the Dutch research council (NWO). You will be part of a team with another PhD student and two consecutive postdoctoral researchers, supervised by Dr. Tim van Erven. This is what you will do: AI and machine
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experiment at the LHC. The project will be conducted in close collaboration with Dr Flavia de Almeida Dias and a postdoctoral researcher in experimental particle physics. Your appointment will be for four
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supervision. Complete and defend a PhD thesis within the official appointment duration of four years. Your profile An MSc degree in Artificial Intelligence, (Applied) Mathematics/Physics, Computer Science
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experiment at the LHC. The project will be conducted in close collaboration with Dr Flavia de Almeida Dias and a postdoctoral researcher in experimental particle physics. Your appointment will be for four
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. Your profile An MSc degree in Artificial Intelligence, (Applied) Mathematics/Physics, Computer Science, Engineering or related field. A strong background/knowledge in machine learning and computer vision