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obtained in monkeys on implicit statistical learning within our laboratory. • Mastery and adaptation of bio-inspired Hebbian learning models • Evaluation of the ability of these models to account for data
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(phosphate, oxides) synthesis by solid state reactions, hydrothermal process or electrodeposition. These solids will be characterized through i) diffraction of X-rays (XRD), ii) Scanning Electron Microscopy
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, bioinformatics, or other relevant field - Strong computer skills and expertise in demographic modeling - Fluency in spoken and written English - Ability to work effectively in a multidisciplinary team environment
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, electronic properties, and spectroscopic response. A major component of the project will focus on coupling DFT calculations with machine learning models to accelerate spectral prediction, identify robust
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manual gestures). The SyncoGest project (2025–2030) is an interdisciplinary project conducted jointly by computer scientists (Loria – University of Lorraine / Inria / CNRS), linguists (Praxiling – Paul
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. Fluorescence lifetime is a powerful probe of molecular interactions, chemical environment and local medium properties, both in living cells and in nanostructures or functional materials. The project therefore
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of contributions to the international ePIC (electron Proton-Ion Collider experiment) collaboration associated with the construction of the future Electron-Ion Collider (EIC, Brookhaven National Laboratory -BNL, New
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system combining piezoelectricity and relatively high mobility electron gas: AlGaN/GaN. This will enable several functionalities, the first one being the ability to modulate the 2DEG electronic density
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devices using broadband spectroscopy and electron microscopy The position offers a stimulating interdisciplinary environment and opportunities to develop advanced skills in numerical optimization
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FieldComputer scienceYears of Research Experience1 - 4 Research FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria Skills/knowledge: computer vision, neural networks