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Field
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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a
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Start date 1 October 2026 Additional Funding Information This project is awarded with a 4-year Norwich Research Park Biosciences Doctoral Training Partnership PhD CASE studentship with Inspiralis Limited
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, Imperial, Swansea, York) and the UK Health Security Agency. CHILI uses energy and air quality modelling, indoor environmental quality data, large health data sets, and cocreation and engagement with schools
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for appointment at Grade 7 with a salary range of £39,424- £47,779 per annum with amended duties and responsibilities. About us The MMM Unit is based at the Big Data Institute and John Radcliffe Hospital. We work
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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ability to evaluate fossil fuel CO2 (ffCO2) emissions is currently limited. ‘Bottom-up’ emissions estimates, based on inventory-style accounting and mobile tracking data, can differ significantly from each
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. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience studying bumblebee behaviour
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Please note, this is a PhD Scholarship co-funded by Foundations, the National What Works Centre for Children and Families and the biggest funder of evaluation research in early interventions
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experimental chemistry. Additional training in a wide range of soft and hard skills is available at the University. What you will do: This PhD project aims to bring together multi-scale modelling and data-driven