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, funded by the ITMO Mathematics and Informatics for Cancer program, is to efficiently process and analyze hundreds of thousands of RNA-seq datasets on human cancer. The central innovation of the project is
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bioinformatics approaches to human-relevant models and unique patient-derived tissue cohorts from colorectal cancer. The aim is to dissect the tumor "ecosystem" with a particular emphasis on epithelial cells
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experiment. Finally, bioinformatics will be used to compare the sequences enriched in each experimental context (topology, addition of chemical modifications, medium composition). The most promising constructs
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; - interact closely with the team of Dr. François Robin (Sorbonne University), project partner for the development of experimental results and bioinformatics approaches; - develop statistical tools
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 25 days ago
Post-doctoral Position In Medical Image Processing The Computer-Assisted Medical Interventions (CAMI) team at the TIMC laboratory (Grenoble, France) is seeking a highly motivated postdoctoral researcher
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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researcher will join the bioinformatics team at the MMSB Laboratory (UMR 5086; MOMS team). Responsibilities: (1) computer simulations as part of the SELDOM project, focusing on interactions between lipids and
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the complexation process. In parallel, these systems will be evaluated as organocatalysts, with the ultimate goal of achieving highly enantioselective transformations. Candidates should hold a PhD in chemistry and
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with the totality of ISEM research groups and facilities, particularly the PEM and EvoDemo groups, and the Biodiversité Moléculaire and Bioinformatic facilities. Main mission : Produce theoretical and/or
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on estimated movements using eDCCs. The research will focus on data simulated using the Monte Carlo method and real data from clinical SPECT scanners with a parallel collimator, such as those available at LUMEN