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prediction of DNA replication defects and mitotic errors in human IVF embryos from live cell imaging data. In addition, you will contribute to teaching activities (maximum 0.1 FTE) and supervise Bachelor and
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PhD thesis student (f/m) at ESRF and University of Bath to study CO₂ capture by coupling X-ray Raman
of Bath and the soft X-ray imaging beamline, I08 SXM, at Diamond Light Source, giving you hands-on experience in material synthesis, carbon capture testing, and spatially-resolved X-ray imaging. Together
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fundamental learning procedures to tackle distressing images related to aversive memories. The aim is to generate insights with direct impact on clinical practice and patient wellbeing. PhD Candidate Reducing
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Description Do you wish to join the new Marie Sklodowska-Curie Action Doctoral Network PREFERENCE and help us make a transformative difference in the field of molecular imaging? Then, apply to join the
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molecular complexes during cellular transitions. Your project will apply cutting-edge proteomics, polysome profiling and imaging technologies such as cryo-EM, cryo-ET to study changes occurring in plant
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. You will work alongside PhD candidates, postdocs, and students in a supportive environment where scientific curiosity is actively encouraged. We value open discussions, shared progress, and celebrating
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
the Dutch aviation sector and public policy. Within this unique ecosystem, you will join a diverse and interdisciplinary team of twelve PhD candidates and three postdocs from across the Faculty of Aerospace
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conditions. The team is young, interdisciplinary, and collaborative. You will work alongside PhD candidates, postdocs, and students in a supportive environment where scientific curiosity is actively encouraged
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work alongside PhD candidates, postdocs, and students in a supportive environment where scientific curiosity is actively encouraged. We value open discussions, shared progress, and celebrating scientific
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households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data