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at the University of Luxembourg. She or he is expected to actively contribute to the scientific activities of the research group, including participation in seminars, workshops, conferences, and collaborative
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conferences. • Contribute to the writing of scientific publications. Optional : • Design Machine Learning (ML) potentials. • Code in FORTRAN and PYTHON to improve the functionality of the global
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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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to the LabNBook and UNESS platforms (more than 60,000 cumulative users). Close collaboration with AI engineers, doctoral students, post-docs, and academic partners is planned. The candidate will work on the
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signal-to noise Post-processing: denoising, reconstruction algorithms Comparison with high-field MRI: deep-learning and other AI modalities for low-field MRI optimization Close cooperation with
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. The researcher will collaborate closely with Dr. Matthew Blakeley from the Large-Scale Structures group, who is responsible for the neutron macromolecular crystallography beamlines LADI and DALI at ILL. Candidates
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colleagues, in order to acquire new skills. • Design and synthesis of new molecules with photocrosslinking functionalities that can self-assemble on surfaces according to compatible patterns. This task will
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of the project is to exploit such data to develop generative models for aptamer design. The candidate is expected to have a strong background in machine learning and statistical physics, with a real interest for