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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify
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& Complexity group - Participation in the research seminar and group discussions - Supervising interns and collaborating with PhD students The team has expertise in quantum algorithms and quantum complexity
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of axion production in dense media, with a particular emphasis on gamma-ray signatures, gravitational waves, and multi-messenger observables. Qualifications: Applicants should have: a PhD in physics or
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molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
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to conduct his own research projects if the scientific scope is compatible with the ERC ATTRACTE (modulo machine time). This position is funded by the ERC Starting Grant ATTRACTE project (2023-2028, PI: G
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- 4 Additional Information Eligibility criteria Essential skills - PhD in a relevant field (cognitive neuroscience, psychology, linguistics, psycholinguistics) - Experience in EEG and/or fNIRS
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of Learning and Development (LEAD- UMR-5022), at the Université Bourgogne Europe, CNRS (https://lead.ube.fr/ ) . To apply, please submit: - CV - Cover letter describing your interest in the position
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for the rapid and effective treatment of severe traumatic wounds, particularly in civil or military emergency situations. This new system aims to combine strong adhesion in wet environments, on-demand UV
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2026. This position is funded by the ENIGMASS+ federation and is primarily intended for early-stage postdocs with up to two years of experience since obtaining their PhD. We are seeking highly motivated