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for the interpretation of neural network predictions and outputs, including concept-based activation and conterfactual analyses. We are looking for a motivated postdoctoral researcher to join the AI for Genome
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to identify and model the most efficient catalytic sites on NDs using advanced Density Functional Theory (DFT) calculations. The project seeks to revolutionize the design of ND-based catalysts by controlling
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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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motivated postdoctoral researcher to develop and apply imaging-based and computational approaches to identify predictive nuclear signatures of cell fate. The project combines experimental and quantitative
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, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling
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-performance single-photon sources based on semiconductor quantum dots (QDs) in cavities [1]. In particular, we have developed efficient interfaces between a single material qubit (the spin of a single charge in
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procedures to the research engineers recruited for this task, based on the established annotation protocol; o Overseeing the synchronization week organized to harmonize and ensure consistency in the annotation
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly
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the development of new bio-based production pathways. One innovative approach involves coupling furans, derived from lignocellulose, with light olefins via the Diels-Alder (DA) reaction to obtain aromatic monomers