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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
-tier machine learning and computer vision venues, actively participating in departmental seminars, and contributing to collaborative projects. Where to apply Website https://jobs.inria.fr/public/classic
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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analysis, computer vision, or machine learning, with a clear interest in developing image analysis algorithms and an affinity with medical topics. Good communication and organizational skills are essential
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image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial intelligence models would be advantageous What We Offer The appointee would be exposed to ample
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Specific Requirements The ideal candidate has the following qualifications: - interest in human cognition - experience with neural data, especially EEG or MEG - experience with machine learning models
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Researcher position. The selected candidate will work on research and development for testing, analysis, and integration of AI and machine learning (ML) algorithms in new simulation and training architectures
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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biosafety level 2 (BSL2+) lab), immunology (such as flow cytometry and CAR-T engineering), molecular biology (such as CRISPR, RNA biology), or animal models, (e) collaboration with other researchers both
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well as exploring the application of research findings to advanced 3D models such as organoids and 3D bioprinted tissues Learning about high-content, automated phenotypic drug screening pipelines against high