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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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are to: Develop a probabilistic machine learning tool that can determine the optimal grinding parameters for different scenarios based on required material removal depth and rail grade. Generate data through
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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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Learning Centre; A complete educational program for PhD students; Multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses; 7 weeks
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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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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: 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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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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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