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-based participatory research, behavioral health, occupational health, environmental health, climate change, public health informatics, or structural and social determinants of health. Deep knowledge and
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paradigms centered on human perception. Finally, the recent rise of foundation models and multimodal artificial intelligence opens up new perspectives at the interface between coding and machine learning
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Expertise: Familiarity with supervised/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g
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. The salary range for this position is $30.89 - $49.68 (Hourly Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 2 months 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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scholarship + CEITEC PhD School scholarship. Requirements on candidates: Motivated smart people that have the “drive” to work independently, but also willing to learn from other people in the lab and
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Statistics → includes enhanced salary compared to standard PhD funding in Switzerland. Website for additional job details https://www.izb.unibe.ch/about_us/open_positions/index_eng.html Work Location(s) Number
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candidate will undertake tasks in WP2 on the AI4MAP project focusing on developing deep learning models for ecological perception and characterisation of Posidonia oceanica seagrass. The tasks include
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within the framework of project FLUIDNET: A Novel Deep Learning Architecture for Solving Viscoelastic Fluid Flows with reference 2024.13980.PEX, f, funded by national funds through FCT/MCTES, under
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Physics with applications to Material discovery and quantum chemistry - Gen AI designing materials with target properties Astrophysics and cosmology: Deep learning for telescope image analysis