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in Artificial Intelligence (Machine Learning and Statistics) at CentraleSupélec, · Joël Eymery, Head of the Nanostructures and Synchrotron Radiation Team at CEA Grenoble, · Jean-Sébastien
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evidencing: (i) which scientific discoveries are more impactful than others; (ii) whether public attitudes to science change over time; (iii) how the public learn and talk about science; (iv) how different
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of remedial rules and institutions. Reframing remedies as an intermediary link between different systems crucial in the production of our imaginaries of justice, CURE aims to provide a new reading of labour law
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Pneumatic Tires, Structure-Process-Properties Relationships. As part of it, we are currently looking for a postdoc on machine learning for road characterization. How will you contribute? Do you have proven
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and
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in Dr. Shanlin Ke’s lab. The overarching goal of Dr. Ke’s lab is to develop computational approaches and leveraging bioinformatics tools, metagenomic sequencing, multi-omics data, machine learning, and
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evidencing: which scientific discoveries are more impactful than others; whether public attitudes to science change over time; how the public learn and talk about science; how different target groups respond
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 hours ago
biogeochemical model using times series forecasting and machine learning. The Post Doc will focus on one or two of the questions depending on their expertise and interest. Minimum Acceptable Education & Experience
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programs, services, and activities. Syracuse University has a long history of engaging veterans and the military-connected community through its educational programs, community outreach, and employment
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have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding of neuroimaging data to predict subjective