11 machine-learning-"https:" Postdoctoral positions at University College Cork in Ireland
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criteria: 1. A PhD in Engineering or similar discipline 2. Strong engineering and analytical skills 3. Computer modelling skills (i.e. MATLAB or Python) 4. A comprehensive
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. Strong engineering and analytical skills 3. Computer modelling skills (i.e. MATLAB or Python) 4. A comprehensive understanding of statistics/statistical modelling 5. Backgrounds in
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health Duration: 12 months, full-time post Where to apply Website http://www.ucc.ie/en/ Requirements Research FieldBiological sciences » OtherEducation LevelMaster Degree or equivalent Skills
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semimetals, magnetic monopole fluids, quantum spin liquids and exciton fluids. • Data Management, Analysis & Machine Learning Knowledge and experience in high volume image-array data acquisition and management
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fluids, quantum spin liquids and exciton fluids. • Data Management, Analysis & Machine Learning Knowledge and experience in high volume image-array data acquisition and management, development of custom
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of Pharmacy, UCC, led by the Professor of Pharmaceutics, Brendan Griffin is aiming to develop Artificial Intelligence (AI) and machine learning tools that can accelerate the creation of medicines for conditions
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MRIs (available in HIE cases) and radiomic features predictive of school-age MRI extracted. Machine learning algorithms of radiomic features predictive of future brain development will be developed
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–machine collaboration will ensure that factories remain productive, sustainable, and resilient even in the face of volatility. FLARE addresses significant challenges related to process design, system
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–machine collaboration will ensure that factories remain productive, sustainable, and resilient even in the face of volatility. FLARE addresses significant challenges related to process design, system
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scheduling, self-healing control systems, and human–machine collaboration will ensure that factories remain productive, sustainable, and resilient even in the face of volatility. FLARE addresses significant