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catalytic strategies for the formation of C–C and C–X (X = N, O, S) bonds through inter- or intramolecular coupling reactions using original substrates. A particular focus at the beginning of the project
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structural data, which requires laboratory based as well as on-site analyses. The main objectives are as follows: Compile legacy data of stained glass windows, this requires extensive data curation and
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to develop a new generation of fluorescence microscopy methods aimed at overcoming current limitations in acquisition time for single-molecule imaging. The project explores original strategies for temporal
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team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed
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HTML user interface for visualising the final data resulting from the screening. - Contribute to the development of an original method for searching for pharmacophores based on raw data from virtual
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, literature analysis, and real-condition experimentation. Main Objective: Investigate the thermodynamic, kinetic, and operational conditions influencing the formation of certain compounds in an industrial
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boson in 2012, the question of what lies beyond the Standard Model (SM) of strong and electroweak interactions has become the primary target of the LHC physics program. With no clear signal observed so
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liquid phases. The main objectives of the study will be the design of catalysts and the understanding of their catalytic properties. A rigorous investigation of reaction kinetics and mechanisms will be
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closely with other post-docs and researchers involved in the DEEP-C project. • Dissemination: Publish research findings in scientific journals and present results at conferences. Supervisor's team Main
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approach, Timelapse-Patch-Seq (TiPS), combining electrophysiology, 3D imaging and transcriptomics at the level of individual synapses. Main tasks Design and conduct a postdoctoral research project on long