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the PhD student using technics such as genome editing (CRISPR), RNA sequencing, use of primary samples, functional studies with various in vitro and in vivo mouse models. The research is also relevant
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 1 month ago
disseminate the developed methods. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09574 Requirements Skills/Qualifications PhD in Computer Science, Machine Learning, Bioinformatics
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(PyTorch, scientific Python) with solid experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image
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Job Description Do you want to figure out why Bayesian deep learning doesn’t work? And afterwards fix it? At DTU Compute we are working towards building highly scalable Bayesian approximations
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 7 days ago
to develop your innovation and entrepreneurship skills at the cutting-edge of AI/ML and biomedical research? If yes, apply now of our 15-20 fully funded PhD positions here: https://apply.cemm.at/ Supported by
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? Please apply no later than 18 January 2026 via the application button and upload the … Where to apply Website https://www.academictransfer.com/en/jobs/356797/phd-position-in-deep-learning-f… Requirements
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PhD project, the successful candidate will develop an open-source workflow using deep learning and hierarchical statistical models to streamline the data flow from acoustic recorders to ecological
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the world’s largest supercomputers (Polaris, Aurora) and some of the most advanced characterization tools in the world at Argonne and Sandia National Labs. Candidates with a background in deep learning
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and Applications, R Language for Statistics, Data Mining with Big Data, Artificial Intelligence and Deep Learning, Statistical Reasoning for Business Decisions, Data Visualization; Highly Desired: Text
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disciplines associated with geography, soil sciences, hydrology, civil engineering, or related discipline, with research expertise in geospatial AI, deep learning foundation models, hydrology, river science