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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
/Qualifications Strong proficiency in at least one of these domains: astrophysics, data science (statistics, inference, and machine learning), or physical remote sensing/Earth observation. Strong skills in
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://postdoc.wustl.edu/prospective-postdocs-2/ . Lab website: https://cruchagalab.wustl.edu/ . Research Projects: Plasma, CSF and Brain Proteomic analysis. Biomarker identification through the use of machine learning
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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; Distributed multi-agent sensing and cooperative positioning algorithms; Machine learning and data-driven methods for ambient awareness. Working Environment: The PhD will be conducted at the University
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Computer Programming · Ethics and Policy Questions: Genomics, Healthcare and Big Data · Introduction to Applied Data Analysis · Advanced Concepts in Computer Programming
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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
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learning university. In this competition, experience in research and/or development in Large Language Models (LLM) and their respective applications is valued. Where to apply Website https
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research and education directed towards the end-user. Your work tasks This position will involve working with real-time graphics, computer vision, applied AI and integrating Large Language Models (LLMs
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to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience
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democratization of approaches using artificial intelligence based on Machine Learning (statistical AI), data lakes have also been proposed [4,5]. Objectives: Monitoring farming and agronomical activities is based