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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 1 month ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional
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undergraduate students in independent research projects through a summer program for incoming transfer students. Find out more about the lab at https://coevolving.org . REQUIRED QUALIFICATIONS: o A PhD
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Multisport card Selection process Interview via Zoom with selected candidates. Website for additional job details https://www.mff.cuni.cz/en/physicsphd/f8/phd-projects-funding/internal-climate-… Work Location
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relevance to extraterrestrial environments and future energy strategies. For more information on this project, see: https://copl.ethz.ch/research/research-projects/2025-saar.html Job description The PhD
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. The project involves large-scale analysis of high-dimensional datasets, including: single-cell and single-nucleus RNA sequencing spatial transcriptomics (e.g., 10x Genomics, Xenium) germline and somatic
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in person in São Paulo and involves single-cell and spatial transcriptomics analysis, integration of multi-disease datasets, investigation of cell–cell communication, and the development
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, the successful applicant will analyze integral-field spectroscopy (IFS) data of starburst galaxies, with a primary focus on the spatially resolved star formation history and on the physical properties
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that combines functional, structural, spatial, and temporal information. This enriched representation will support both static validation and dynamic analysis, including the automatic synthesis of timed and
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prinicpal investigators within the Genetic Center to design and implement analysis pipelines in several broad areas of computational biology including analysis of experimentally generated datasets in basic
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spatial biology. Please include a cover letter with your application detailing your qualifications and experience for this position. Describe a deep learning project you have executed, ideally a creative