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or treatment of Alzheimer’s disease. Position Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing, single nuclei RNA sequencing and spatial transcriptomics
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 1 month ago
Bayesian statistics, AI-assisted inverse problems, planetary remote sensing, and environmental monitoring. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-09787 Requirements Skills
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TLDR: Build the data backbone for the next era of AI-powered spatial biology. Please include a cover letter with your application detailing your qualifications and experience for this position
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(http://vanallenlab.dana-farber.org/) to work on the analysis of new datasets generated in the context of multiple clinically oriented cancer sequencing projects in order help advance efforts
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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 1 month ago
. At the Department of Macroeconomics (Faculty of Business, Law and Social Sciences), we research economic transformation processes and their spatial dimensions. We use our findings to support business
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and philosophers, including one other PhD student (statistics) and two postdocs (spatial forest ecology and philosophy/social science). The candidate is expected to contribute toward developing
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/ Center / PI Job Title: Postdoctoral Associate - Machine Spatial Reasoning Office/Location: SUNY Polytechnic Institute The Center for Safe and Secure AI Robotics is looking to hire a post-doctoral associate
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combining wave modelling, field observations, remote sensing, advanced mathematical methods and AI, the research will quantify spatial patterns of wave set-up, its statistical distributions and co-occurrence
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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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of novel probabilistic deep-learning models that automatically extract mechanistic and statistical knowledge from your in vivo perturbational omics data. This interdisciplinary atmosphere has been a main