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are recruiting three PhD students for the following Projects: Project 1: Unifying on-farm data and crop models to enhance tactical crop decisions Summary: Despite the increasing availability of on-farm data and
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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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modelling astrophysical phenomena. The PhD project will focus on developing theoretical methods to generate accurate data to meet this demand. The student will gain expertise in high-performance computational
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-economic assessment (TEA). This suits Chemical Engineering students. Numerical simulations may also be conducted to assess the reliability of existing modelling tools by comparing them with experimental
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models. TensorFlow or PyTorch is desirable. How to apply To apply, please ensure you have digital copies of the below information: • Curriculum vitae; encompassing any research presentations and/or
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-style interventions, and preventative medication. Analysis will utilise best practice in health inequalities measurement, modern econometric techniques, behavioural experiments, and modelling
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the diverse range of knowledge and skills of CaLD customers and employees. This project aims to use social identity theory and role theory to develop a comprehensive conceptual model for the process by which
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injection into a reservoir saturated by water from core scale up to the numerical cell or a reservoir is a challenge. For immiscible two-phase flow in layer-cake reservoirs, the models for pseudo relative
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instance learning and weak supervision / spatial transcriptomics models to individualise tumour type, associated biomarkers and genomic characteristics to high precision. The resulting multipurpose machine
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to trick AI-based models, pay little attention to fake-normal data traffic generated by Generative Adversarial Networks (GAN). This PhD research will address a major vulnerability in AI based smart grids by