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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Transactions on Machine Learning Research, 2024 Kugler, F. Forbes, and S. Douté. Fast
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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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Candidates MD, DO, MBBS, PhD, EdD, or equivalent in a related field such as the health sciences, education or other field given context of work experience and/or other qualifications. Qualified for a faculty
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and colleagues. Job Requirements: Required Qualifications: PhD in Human Computer Interaction, Computer Science or a related field by time of appointment Documented teaching and research ability
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for machine learning and AI acceleration. Perform performance, power, and area
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Multi-modal Machine Learning—including areas like Neuro-symbolic AI, Knowledge Graphs, Contextual AI, Conversational AI, and Trustworthy & Safe AI. This role also offers the opportunity to explore human
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual