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– NSF CBET project on sustainable computer networks, with a focus on carbon emissions reduction and network telemetry. You will contribute to the development of a framework that reduces the carbon
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timing; challenge outdated legal and regulatory frameworks; test the feasibility of new clinical services; and engage with the public and policymakers to shape a more inclusive future for fertility control
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of random fields, the project seeks to illuminate complex analytic phenomena that remain out of reach under traditional deterministic frameworks. Reporting Structure The PDRA will be based in the Department
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. About You You will have a PhD in computer science, engineering, mathematics, or similar, strong programming skills, and experience with a contemporary machine-learning framework such as PyTorch. You will
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discipline. You must have experience with conducting independent research in AI or data science. You will have strong programming skills in Python and modern machine learning frameworks, experience working
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large-scale genomic, phenotypic, environmental, and field trial datasets, you will benchmark AI and quantitative genetics methods, develop multimodal and hybrid modelling frameworks, and investigate
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ecological transitions needed for coastal communities to adapt to a changing climate. At its core, the project develops and applies the Coastal Regenerative Resilience (CR2) framework, an innovative yet
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. About You You will have a PhD in computer science, engineering, mathematics, or similar, strong programming skills, and experience with a contemporary machine-learning framework such as PyTorch. You will
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commercialisation proposition including an IP licensing framework to develop a minimum viable digital platform prototype to demonstrate scalable CDT delivery. The successful candidate must be able to demonstrate
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how Muslim institutions evolve in interaction with constitutional frameworks, state institutions and wider society, creating a lasting research resource for comparative scholarship on Islam in Europe