45 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" uni jobs at University of Sheffield in United Kingdom
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with the CDT’s aim to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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specialised expertise in the Machine Learning for Engineering sub-theme. Candidates from all areas in machine learning are encouraged to apply, with a special focus on the areas of (i) information theory and
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Automatization and Digital Enhancement of Characterisation Techniques: Joining the Dots between AI, Machine Learning and Materials Advances School of Chemical, Materials and Biological Engineering
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. This project will develop responsive manufacturing technology that will have sufficient flexibility to overcome such problems by utilizing intelligent machine learning to control the printing process in real
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Ability to lead and work in teams Essential Application/Interview Experience and capability in blast computational simulations using codes such as Viper:: Blast, machine learning, and/or LS Dyna Desirable
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(SITraN). Our mission is to uncover the genetic drivers of Amyotrophic Lateral Sclerosis (ALS) by integrating cutting-edge technologies, including single-cell epigenetic profiling and machine learning, with
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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning
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combination of physics-based, and data-driven AI-based approaches employing neural-networks and machine learning, this project will develop and validate a multi-time scale DT concept for advanced condition
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, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a