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PhD Studentship: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin
PhD Studentship Advert Title: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin Supervisors: Prof Ruman Rahman, Dr Stuart Smith, Dr Phoebe McCrorie Project Overview
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Applications are invited for the above post, to start in April 2026 for a fixed term of six months. The successful applicant will work with Dr Alice Thompson on modelling fluid flow and transport in
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the PhD Students to: - Perform the analytical determinations for the different models, including sample preparation, and analysis of various lipid compounds, mineral elements and stable isotopes
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positions at UCD working closely with industry partners, specifically LaNua Medical (https://www.lanuamedical.com/ ) and Integer Holdings Corporation (https://www.integer.net/ ). The project focuses on LaNua
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biochemical datasets. A major focus will be on introducing new AI models to chart the chemical “dark matter” of the mammalian metabolome; examples of such models include large supervised or self-supervised AI
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for students with MSc degree with reference number BI|2026/881 under the scope of the Projet SALVE: Securing Artificial Language Models Against Vulnerability Encoding (2024.14936.PEX), funded by Fundação para a
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Description Are you excited about using large-scale AI to accelerate scientific discovery? Join a Horizon Europe project developing next-generation scientific foundation models that combine knowledge graphs
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organization Knowledge of at least one empirical method (desirable) - Computable general equilibrium modeling and simulation or - Econometrics or - Agent-based modeling Excellent command of English High personal
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developing indices, summary characteristics, and models for marked point patterns (of trees), as well as (goodness of fit) testing using Monte Carlo and permutation methods. Challenges come, e.g., from
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. These triggers can be injected into the training dataset or directly into the model weights. These are then called poisoned. Due to parameter-efficient fine-tuning methods, backdoor attacks on large language