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Open to: UK applicants only Funding providers: EPSRC CASE Conversion with GSK Subject areas: Applied Mathematics, Mathematical Modelling, Mathematical Biology Project start dates: 1 October 2025 1
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“Decoding faba bean yield stability: From in-field image data to mechanistic models (DIGIFABA)”, which is financed by Novo Nordisk Fonden. Start date is (expected to be) 1 October 2025, or as soon as possible
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aims at developing tools using large language models (LLMs) for the correction of misinformation about climate change in social media. The successful candidate will develop innovative tools leveraging
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cells or the tumor microenvironment reduces tumor growth and extends survival in preclinical models, underscoring their potential as dual-function therapeutic targets. To address this, we aim to develop
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experimentation and finite-element modelling. Research themes would be flexible including green steel formability under the EPSRC ADAP‑EAF programme for automotive and packaging applications; or micromechanical
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Applications are invited for a Senior Researcher in Decision Analytic Modelling, working with Associate Professors Apostolos Tsiachristas and Brian Nicholson, on a research programme developing and
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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Applications are invited for a Senior Researcher in Decision Analytic Modelling, working with Associate Professors Apostolos Tsiachristas and Brian Nicholson, on a research programme developing and
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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
. We are seeking a Senior Researcher to lead pioneering work in synthetic biology and metabolic engineering, with a particular focus on non-model prokaryotic microorganisms capable of utilizing power
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading