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Field
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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and
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-driven changes in rainfall. This poses significant flood risk to homes, businesses and infrastructure. For example, during the winter of 2015/2016, mine water flooding at Kibblesworth in Gateshead flooded
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models such as Random Forest and Neural Networks to help understand and predict pairwise interactions between pollinators and plant species. - Software Engineering: integrate models into a standalone
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This PhD project aims to achieve a breakthrough in thermal efficiency (>50%) for dual-piston Free-Piston Engine Generators (FPEGs). The research will pioneer a novel, integrated methodology
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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Software Development: Building the Next-Generation Trust Maturity Model Integrating DevOps practices into ML-driven systems: A Framework and Maturity Model for Continuous Machine Learning Development
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challenges in engineering Desirable: Experience with mathematical modelling, optimisation techniques, or supply chain analysis Background knowledge in bio-based materials, biorefineries, or circular economy
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MEng degree (or equivalent) and a PhD in Maritime Engineering and Technology or pertinent disciplines (Res Assistant if no PhD), adequate knowledge of modelling marine engines operations with alternative
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performance through physics-driven insulation material design. Applicants should hold a first-class (or equivalent) degree in a relevant engineering or science discipline (upper second class may be considered
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are the highest ever recorded, and are rising due to the cessation of mine water pumping and climate-driven changes in rainfall. This poses significant flood risk to homes, businesses and infrastructure