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: Using big data insights to optimise the manufacturing process The second phase of this project will focus on processing and utilising machine-learning techniques to analyse large volumes of data from
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group of PhD researchers who will tackle the most pressing questions in Machine Learning while ensuring AI serves humanity responsibly. You'll work within one of our specialised research themes, each
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the machine-learning infrastructure that integrates CFD outputs with biological datasets. This role bridges engineering, computational modelling, and experimental integration — ideal for someone
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comprehensive metadata schema is followed, consistent with relevant industry standards (e.g., ISO 19115). 2. Geospatial Analysis and Machine Learning: Develop and implement analytical tools and routines
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. Details of the role This Data Scientist role offers a unique opportunity to work at the intersection of artificial intelligence, machine learning, and healthcare. The role will contribute to designing and
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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teaching and learning capability, and contributing to pedagogical research and scholarly practice. To be successful, you’ll have: PhD in Business Analytics, Information Systems or a related discipline, or
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this project, we will develop automated approach to detect the defects in AI systems, including LLMs, auto-driving systems, etc. Required knowledge - self-motivated, willing to spend time and efforts in research
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National Industry Scholarship We are looking for highly motivated students with training background on crop genetics, genomics and biochemistry. Students with training background on AI, machine
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to $5000 (total) for computer / software / conference attendance to present PhD results. Scholarship Details Maximum number awarded 1 Eligible courses Higher degree by Research (PhD) – in Health Sciences