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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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accelerate the global shift to electric mobility. This PhD will build an AI-driven fleet-scheduling framework that learns from battery data in real time and optimises charging, operations and maintenance
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of system and data confidentiality and complete any other requirements. Desirable skills: Experience in programming (C, python, or similar). Knowledge in machine learning or distributed systems. How to apply
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. Understanding of or curiosity about machine learning, AI, or cloud computing tools used in agricultural analytics. Interest or experience in working with industry, government, or multidisciplinary research teams
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expertise in research methodology or willingness to learn. Well-developed computer skills. Application process Expressions of interest are invited to be submitted electronically to Professor Judith Finn via
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the world. Ideal applicants will have a solid background in AI, machine learning, control theory or quantitative finance. Applicants with advanced programming skills (Python/C++); and a desire to publish in
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awardee. The Opportunity Generative artificial intelligence is a significant and highly visible use of machine learning which has become commonplace in a matter of a few short years. Without common
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external enrolment procedures. Selection criteria Demonstrated experience in programming and system development. Expertise in Python programming and data analysis. Experience developing Machine Learning