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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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4D imaging, their high data rates limit their use in real-time applications. As a result, the available benchmark data for developing numerical models to predict transient multiphase flows is limited
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contexts (predicting the desired effect in humans vs. ruling out safety risks); transferability of approaches and models from research into the application of NAMs in the safety assessment of chemical
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is an explicit QoE severity model that learns to rank bugs based on predicted player impact (for example frustration, immersion disruption, fairness issues, comfort or usability), using lightweight
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and ML pipelines for drug synergy, write code for data analysis and post-processing data. Training of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio
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state-of-the-art tools and AI libraries developed at the TU/e, such as GameBus and Experiencer. The collected data forms the basis for developing predictive AI models that tailor coaching content and
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research into predictive failure analysis methodologies for HVDC interconnection systems. This will include assessments of primary components such as switchgear and power transformers, as well as essential
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. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or
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, radiologists interpret scans such as CT, MRI, and X-ray together with textual information, including patient history, laboratory findings, and prior reports. Although recent Vision–Language Models (VLMs) can