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as ligands. The fundamental science behind the selective enrichment of lithium-6 by solvent extraction is poorly understood. This project will combine molecular quantum mechanics and molecular dynamics
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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, or dynamic models to predict gene regulatory interactions. Work with digital twin technology, simulating patient-specific disease progression and treatment responses. Collaborate in an interdisciplinary
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, making a new model which is suitable for a variety of polymer systems. This will involve integrating molecular dynamics simulations, electronic structure calculations, and machine learning techniques
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of cancer cells. The models are trained on high-throughput datasets, including metabolomics, proteomics, and transcriptomics, and constrained to align with the cell’s molecular networks. This allows us to
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to forward a copy of your CV along with your academic referees. Informal enquiries to:Colin.clarke@nibrt.ie or niall.barron@nibrt.ie Funding:Annual stipend of €25,000 plus fees for 4 years.Project 2: Model
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dynamic changes in gene and protein expression as stem cells differentiate into mature blood cell types. This doctoral project focuses on developing computational methods to model cell development
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implementation is advantageous (especially if in the context of protein structure) Experience with structural biology and/or molecular dynamics is advantageous Publicly available code is advantageous Experience
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dynamics of plasmid copy number in pathogenic bacteria, with a strong focus on infection biology and evolution of antibiotic resistance. The goals for this project are i) to better understand the role