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profiles. A successful implementation and completion of this PhD project will provide vital information for making a step change in our hydrogen economy, opening the door for making a lasting impact
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lines and collect high-throughout measurements of corresponding mRNA and protein levels for several protein targets. Ultimately, you will use these data to develop Matchmaker, a machine learning framework
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, data analysis, and scientific writing and communication. Experience in working in an optics lab and programming in Python will be beneficial but is not necessary. Applicants should have, or expect
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operating filters. Quantify operational performance including headloss recovery, filtrate turbidity, biological stability and lifecycle carbon—using high-resolution sensor data and life-cycle assessment tools
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stresses. Based on the experimental data, a semi-empirical model to be developed to assess insulation degradation and identify failure signatures that can inform future predictive asset management strategies
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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be ideally suited to a person considering a PhD, or looking to gain additional research experience. The post is offered on a full time (36.25 hours per week), fixed-term contract until 30 April 2027
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disciplines and sectors. More information can be found at www.ukri.org and www.mrc-lmb.cam.ac.uk. Closing date: 07/09/2025 Final appointments will be subject to a pre-employment screening. We actively support
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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existing data analytics tools will help deploy these technologies in the industry context without the need for big datasets. Predictive Maintenance (PdM) is one of the maintenance strategies that has