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://www.academictransfer.com/en/jobs/354978/phd-position-theory-of-learning… Requirements Specific Requirements You hold an MSc in physics, engineering physics or mathematics. You have a good command of analytical techniques
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single-cell proteomics approaches on patient samples to gain additional resolution on cell-state diversity within tumours. In parallel, you will collaborate closely with a postdoctoral researcher to
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condensed matter physics on one of the topics listed below. You will actively cooperate with other PhD candidates, postdoctoral researchers and staff of HFML‐FELIX working on related topics, and you will be
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-ACCENT project aims to fill this gap by combining insights from cognitive psychology, neuroscience, AI engineering, human-computer interaction and social science, with lifespan perspectives. Using advanced
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, simulations and cutting-edge data to uncover the origins of black holes and neutron stars, linking theory with the latest discoveries in this rapidly growing field. It has been just over a decade since the
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scattering involves reconstructing the properties of a spatial region from how it interacts with waves. It lies at the core of scientific discovery and technology, from radar to nanoscale metrology and
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addresses this challenge in two ways: We investigate the fundamental neural mechanisms that control movement. We explore engineering-based solutions to restore function when these pathways are disrupted. As a
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Technology (PSST), which is funded by the Horizon Europe Marie Skłodowska-Curie Action. You will receive a joint doctorate from Radboud University and from the University of Lisbon. As a PhD candidate, you
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(on Ecological Momentary Assessment [EMA] data), aiming to develop new ways to quantify fast and slow fluctuations of fatigue in real life, based on complexity theory. Then, you will co-design and conduct a study
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on cell-state diversity within tumours. In parallel, you will collaborate closely with a postdoctoral researcher to develop and apply novel proximity-labelling strategies to chart dynamic protein–protein