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languages (C, C++, C#, Python, Matlab), experience with machine learning in robotics or computer vision, a desire to advance resilient, introspective processing architectures in robotics, a desire to work
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for 2 years. You will lead a research team working on the application of software (AI/machine learning techniques), to improve the efficiency of advanced hardware and integrated circuit design. This role
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Carbon Modelling. This position offers an exciting opportunity to make use of the recent developments in AI and machine learning algorithms by measuring soil properties rapidly over a large area using cost
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, fixed term period for 2 years. This role will help establish a research programme in statistical, data-science, and/or machine-learning approaches to stellar and exoplanetary physics, broadly interpreted
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on the ground robot for real-time soil property assessment, providing critical below-ground data to complement above-ground phenotypic analysis develop machine learning algorithms for real-time analysis of plant
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science and lipids. Experience with AI and machine learning is preferrable but not mandatory experimental skills on microfluidics, analytical techniques, microscopies (fluorescent, confocal and electron
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oscillations, delta Scuti stars, exoplanets, stellar clusters and associations, and machine learning. We make extensive use of data from NASA’s Kepler,TESS and JSWT Missions, and also have access to ground-based