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, the adoption of Machine Learning (ML) techniques for the analysis of archaeological data sets is rapidly increasing [Mackenzie, 2017, Mesanza-Moraza et al., 2020, Bickler, 2021, Palacios, 2023]. ML applications
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Keywords: theoretical biophysics, machine learning, kinematics, (structural) biology. Context. Machine learning techniques have made significant progress in prediction of favourable structures from
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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Leveraging the spatio-temporal coherence of distributed fiber optic sensing data with Machine Learning methods on Riemannian manifolds Apply by sending an email directly to the supervisor
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should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference, clustering, classification • deep learning, variational
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(History, Archeology, …). Expected skills: The candidate should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference
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Apply by sending an email directly to the supervisor Primary discipline: Machine Learning Secondary discipline: Neuroscience Project Summary This project proposes to explore how the brain and
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-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1- Context and Objective: Monitoring tumor response using clinical imaging, such as CT or FDG-PET, has become a
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/CT imaging Description of the topic As this is an interdisciplinary "AI and medicine" project, co-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1
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advances in machine learning and data-intensive approaches facilitate the search for better or even global minima via evolutionary computations or reinforcement learning. Objectives. The main scientific