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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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learning [3,4]. Indeed, the sensors can be represented as a graph, where each node corresponds to a sensor and the edges represent the relationships between the sensors. These relationships can be spatial
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
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. The opportunity: We are looking for someone to analyse environmental soundscapes using signal processing or machine learning. Initially, you will work with underwater passive acoustic sound data, and later may
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trustworthy machine learning, with a particular emphasis on mechanistic interpretability and its application to healthcare data. The successful candidate will contribute to understanding how modern machine
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(HIMS), in close collaboration with industrial partner BOR-LYTE and Smart Industry testbeds. This position offers a unique opportunity to combine inorganic chemistry, spectroscopy, machine learning, and
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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science. Familiarity with ship data processing, ship performance analysis, machine learning algorithms (Deep learning, Reinforcement learning, etc.). Proficiency in written and spoken English - essential for data
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neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches). * Demonstrated track record in multiscale