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of technological disruption driven by Artificial Intelligence, we propose to analyze the data and quantify these similarities by exploring various applications of machine learning methods. With the advancement of AI
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are required. Knowledge and skills in statistics, data analysis, machine learning, conceptual modelling, knowledge engineering, and programming are also required. A first experience with Human-Centered
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to align neuromorphic algorithms with the physical constraints of the target hardware. This hardware–software co design effort will involve: • Deepening and extending NSS-related machine learning and
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, or machine learning) Experience working with various animal models Ability to work in an interdisciplinary research environment Specific Requirements Programming experience (Python and R) Experience with
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IRCM - Cancer Research Institute of Montpellier | Montpellier, Languedoc Roussillon | France | 2 months ago
dedicated to developing new antibody-based biotherapies and diagnostic tools for solid tumors, auto-immune diseases and emerging viruses. The team uses in vitro selection approaches and home-made designed
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of deep learning for computer vision (segmentation/object detection); ability to manage, clean and document large datasets; interest in glaciology, quantitative geomorphology and/or remote sensing
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modern machine-learning techniques, will be exploited to improve the discrimination between the different polarization states. The analysis will use the complete Run 2 and Run 3 datasets collected by
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for a PhD project focused on advancing the design and functionality of deployable structures through the integration of 4D printing, architected materials, and scientific machine learning (sciML). A part
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. These complementary datasets will be integrated using multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience and to reveal the interactions linking microbiome
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computational fluid dynamics. • Experience in modeling, uncertainty quantification, or statistical methods. • Experience in data science or machine learning is considered an asset. • Experience with high