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9th January 2026 Languages English English English The Department of Physics has a vacancy for a PhD Candidate in AI-Driven Computational Modeling of Catalytic Mechanism Apply for this job See
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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of the vibrational energy. This project is intended to perform numerical analysis and modelling aimed at the optimisation and development of effective friction dampers. The research studies on friction damping will be
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for Animal Cancer Study: From biobank to AI-Driven Predictive Models”, operation no. NORTE2030-FEDER-02687800, approved under the call NORTE2030-2024-84 – SACCCT – Support System for the Creation of Scientific
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
-latency, and scalable operation in aerial 6G networks. In this regard, Large Language Models (LLMs) have recently emerged as a key technology to achieve adaptive 6G spectrum management. The core idea of LLM
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, stratification, and prognostic biomarkers. The candidate will investigate EV edition and loading for therapeutic applications, explore cargo composition, and functional impact in disease-relevant models, while
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field of multiphase flow modelling. Contact Dr Nadimi (sadegh.nadimi-shahraki@ncl.ac.uk) for more information. Number Of Awards 1 Start Date 1st October 2026 Award Duration 4 Years Application Closing
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models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify biofouling impacts on flow-induced vibration phenomena, structural
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basic models and patient-derived specimen with cutting-edge technologies. One of our recent projects has been to better understand human polyneuropathies by using single nuclei and spatial
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small code development will be necessary to implement actuator disc/line wind-turbine models. This approach facilitates a deep understanding of the flow physics surrounding MS formation. You will develop