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supports tasks such as predictive modeling, anomaly detection, and synthetic data generation. The models developed are expected to exploit metadata to guide and condition image analysis outputs. By
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believe that generative pre-training offers a promising path to a new class of models that work across settings and can support prediction of many different clinical outcomes at once. To fuel your models
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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead
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derived spheroids Develop predictive model of drug response by comparing 2D to 3D cellular systems Testing and validating the relevance of such models in patient tumour specimens Support and preparation
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wide variety of translational neuroscience research programmes. The focus of the role will be analysis of large clinical datasets from PRECISION-ALS (n~20,000) and PRO-ACE to develop prediction models
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of effluents (collaboration with wastewater treatment plants and industries). Analytical monitoring (HPLC, LC-MS, spectrofluorimetry, toxicity tests). Modeling: Development of predictive models for process
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to this research line, planning protocols, overseeing data collection, facilitating communication between teams, and ensuring ethical and regulatory compliance. Implement data-analysis models to predict cognitive
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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understanding of the underlying physical mechanisms and to leverage this knowledge to develop predictive tools for optimizing the design and control of wind farms. Research scope and responsibilities Depending