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experimental data from both literature and in-house experiment results Use state-of-the-art machine learning models to develop a multi-scale droplets evaporation model Assists in co-supervision of Final Year
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, of 28 of August, and also the provisions of article nº 9 of the Scientific Research Grant Regulations of the University of Aveiro. 5. Work Plan: Intelligent and modular controller with machine learning
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The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model
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team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast-accurate-portable-diagnostics/. What you’ll
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research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu/ . For questions, please email mweber@seas.harvard.edu Applications will be reviewed on a
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for developing machine learning models for the automatic identification of species from images collected through electronic monitoring systems (Work Package 3 – Bycatch Monitoring). The candidate will be involved
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 15 days ago
accessed using the link above. Jiawei Zhou Ph.D. The candidate will be co-mentored by Dr. Yanguang (Carter) Cao and Dr. Jiawei Zhou to advance the development of artificial intelligence/machine learning (AI
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
intelligence/machine learning (AI/ML) and pharmacokinetic/pharmacodynamic (PK/PD) models that reduce reliance on animal testing and enable the quantitative evaluation of safety and efficacy for antibody and mRNA
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world are facing dramatic upheaval as a result of rapid technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic
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Area: Computer Science 2. Admission Requirements: Graduates (Licenciatura) in computer engineering or related area, with experience in Machine Learning/Deep Learning methods/techniques. 3. Project