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lead and support multimodal data collection, data integration, predictive modeling, and digital intervention development. The position involves close collaboration with faculty, clinical partners
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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PhD students. The MoM section is recognized worldwide for its high-level research on experimental analysis, theoretical understanding and predictive modelling of complex thermo-mechanical behaviour (e.g
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University of California, Los Angeles | Los Angeles, California | United States | about 17 hours ago
mechanisms associated with immune phenotypes, as well as development and application of machine-learning and statistical models for classification, prediction, feature selection, dimensionality reduction
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, emerging instructional models, and innovative educational practices that strengthen student learning and institutional competitiveness. Recruit, develop, empower, and retain a high-performing faculty while
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the development of highly predictive and human-relevant biomedical models that overcome major limitations of conventional animal models. The recruited candidate will focus on the development of an advanced human
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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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of their development and structure. Working closely with colleagues at the Universities of Leeds and Reading, you will integrate theoretical understanding, observational data, and modelling approaches to improve
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translational model based on patient-derived tumour organoids that reproduce the realistic microanatomy of the tumour. These organoids will be used for translational testing of nanotherapeutic activity
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Outer Membrane Vesicles (OMVs) from attenuated, genetically modified Salmonella strains; - Key activities involve applying reverse vaccinology pipelines (in silico epitope prediction and 3D molecular