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intelligence, machine learning and data science. You will play an active role in the research team, publish papers, take part in workshops, public events and other activities. The candidates should have a
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increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal
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(e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which
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governments and institutions to achieve outcomes traditional approaches cannot. Job Summary The Bike Shop is hiring a Machine Learning Researcher. The Bike Shop is a CS and Economics research lab focused
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on epithelial barrier integrity, inflammation, and host transcriptional responses. The project offers interdisciplinary training in bioinformatics, advanced statistics and machine learning, anaerobic microbiology
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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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language models, with hands-on experience in training, fine-tuning, or evaluating state-of-the-art models Solid knowledge in adversarial machine learning or trustworthy AI, including experience with
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 19 days ago
solutions exploit low-level landmarks (i.e., points or lines), dealing with large-scale and visually ambiguous environments remains highly challenging due to the inherent multiplicity, ambiguity and
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SD-25045 – PHD IN HONG-OU-MANDEL INTERFERENCE AND ENTANGLEMENT WITH COLOUR CENTRES IN SILICON CAR...
to effectively and reliably coordinate with the supervisor and multiple collaborators Language skills · Good level both written and spoken English Your LIST benefits · An organization with a
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this goal, doped-diamond systems will be considered. The thermal stability of selected compounds under operating conditions will be assessed by means of molecular dynamics simulations with Machine Learning