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Overview We have an exciting opportunity for a motivated and enthusiastic individual interested in biodiversity, evolution, neuroscience and machine learning to join the Leverhulme Trust funded
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BBSRC Yorkshire Bioscience DLA Programme: Decoding Condensin Regulation: Single-Molecule Tools to Target IDR Interactions (CASE project) School of Biosciences PhD Research Project Competition Funded
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academic research in machine learning and computer vision with direct industrial application. You'll be tackling the real-world problem of data scarcity by developing novel methods using synthetic data
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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 monitoring and
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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
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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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-disciplinary team including PhDs, RF-engineers, other Research Associates and ecologists, giving you the opportunity to learn new skills and experience and publish in several fields. You will be based in
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Design of a Fault Detection System for AI-Assisted Adversarial Attacks on Industrial Control Systems
AI-assisted adversarial attacks. You will work on topics such as cybersecurity, intrusion detection, adversarial machine learning, industrial automation, digital twin technology, and reinforcement
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3D embryo images (published and those generated in the Strawbridge Lab). to quantify cell numbers and lineages. A semi-automated pipeline using deep-learning-based segmentation (Cellpose-SAM), machine
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preferences for them using birds as a model system. Capitalising on recent advances in computational neuroscience and machine learning, specific objectives are to (1) quantify common design features of avian