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shift a critical part of the spatial and photophysical information into the temporal domain, in order to drastically reduce the number of photons and the acquisition time required for image reconstruction
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. The objective is to combine ultra-high-precision spatial localization, reduced acquisition times, and access to key photophysical parameters enabling characterization of the local environment of fluorophores
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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of multi-modal Foundation Models that integrate single-cell omics with spatiotemporal information. The second position will address the development of a virtual tissue model, exploiting spatial
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-machines and nano-robots as a drug delivery vehicles. As a core research activities, the postdoc candidate activities are the: - Design and in silico modeling of DNA origami nanostructures with high
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of ASICs dedicated to the readout of AC-LGAD (Alternating Current coupled Low-Gain Avalanche Diode) sensors, capable of very good timing (~30 ps) and spatial (~20 um) resolutions, which will be exploited by
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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melt than greenhouse gas emissions. - The high-resolution regional atmospheric chemistry-transport model CHIMERE will be used to understand the impact of terrain complexity and the spatial variability