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of machine learning algorithms are of real interest in improving the accuracy of water quality measurements, particularly in identifying, accounting for, and neutralizing ionic interference. The second key
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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without neurons in physical systems, Ann Rev Cond Matt Phys14, 417 (2023) [4] Dillavou, Beyer, Stern, Liu, Miskin and Durian, Machine learning without a processor: Emergent learning in a nonlinear analog
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experience with advanced signal processing concepts as well as digital filters is advantageous. Your workplace You will be working at the Division of Electronics and Computer Engineering (ELDA), which conducts
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solid understanding and background in forest management, as well as strong technical skills (programming) and experience with some forms of machine learning. The candidate should be interested in working
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PhD: Systematic Exploration of Robot Behaviours for Manufacturing Tasks to Automatically Discover Failure Scenarios EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering
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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in
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expertise in research methodology or willingness to learn. Well-developed computer skills. Application process Expressions of interest are invited to be submitted electronically to Professor Judith Finn via