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with expertise in areas such as Machine Learning and Deep Learning, Generative AI, Data-Centric and Responsible AI, or AI for real-world applications (e.g., health, cybersecurity, robotics, or business
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modelling and oomph-lib for continuum mechanics simulations, enabling the integration of discrete and finite element methods. Coupled with machine learning techniques, this approach will address the complex
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industries like pharmaceuticals, food processing, and construction, the project may also incorporate machine learning methods for model calibration and optimisation, driving more sustainable material handling
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/physics or any related discipline. This is a largely experimental research project based at the University of Nottingham, with some aspects of material modelling and development of machine learning to aid
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Coordinate recruitment, HR administration and workload planning, acting as a key operational link between academic staff and professional services teams (e.g. HR, Finance, Marketing, IT, Centre for Learning
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foundational knowledge in signal processing and machine learning. Working knowledge of computer vision and deep learning concepts, including object detection and image-based classification, with hands
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for operational decision‑making, interactive design, or control‑in‑the‑loop visualisation. Machine‑learning surrogates offer speed, yet purely data‑driven models often extrapolate poorly and may violate physical
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experience with digital technologies such as cloud platforms, data analytics tools, automation tools, or AI/ML frameworks. - Significant experience applying advanced analytics (e.g., machine learning, NLP
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using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome
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Associate will work on projects coordinated between both experimental chemistry and machine learning academics. The project will be computational in nature, but the post-holder will work together