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be based in the School of Computer Science and Statistics at Trinity College Dublin, Ireland and will be funded under the Science Foundation Ireland Investigators award project. The researcher will
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will contribute to our ability to understand and predict this critical component of ecosystem function. You will work within group spanning ecological science, engineering, and computer science methods
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risk. These studentships are ideal for candidates with a strong background in computational biology, bioinformatics, population genetics, and statistical genetics and an exceptional interest in
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also of growing interest to industry.Project supervisor: Dr. Eoin Delaney (Trinity College Dublin).Project locations: School of Computer Science and Statistics, Trinity College Dublin.PhD structure
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Trinity is Ireland’s leading university and is ranked 87th in the world (QS World University Rankings 2025). Founded in 1592, the University is steeped in history with a reputation for excellence in education, research, and innovation, which has been inspiring generations of thinkers for over...
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scientists from, e.g., Sociology, Political Science, or Economics. The PhD candidate will also develop state-of-the-art optimisation algorithms specifically tailored to the proposed large-scale network models
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) Computational Fluid Dynamics (CFD) Modelling [may involve groundwater modelling with heat transfer depending on the chosen candidate] 3) Energy calculations and life cycle assessments (LCA analysis) 4) Conducting
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Post Summary We are pleased to invite applications for two fully funded PhD studentships to work on a project at the interface of synthetic organic chemistry, electrochemistry, and sustainability
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Molecular Rheumatology, Trinity Biomedical Sciences Institute, TCD (https://rheumatologytcd.com) is seeking to appoint a PhD student to perform research focused on identifying molecular pathways
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for a PhD studentship to investigate the genetic underpinnings of neuropsychiatric disorders using cutting-edge long-read sequencing (LRS) technology. This project aims to generate whole-genome LRS data