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3.5-year D.Phil. studentship Supervisor: Dr Thiago B. Burghi This project will develop data-driven models for predicting seizure-like events in living neural networks. Such events can be induced in
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to predictably control and exploit the drop for useful tasks. Aims: Develop computational models to quantitatively predict the response of chemically active drops to the various physico-chemical stimuli
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candidate (m/f/d) for the “Development of a digi-tal twin and model predictive control for filtration processes”. Project Join a research initiative in the field of Industry 4.0 and cyber-physical systems
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for age-related macular degeneration (AMD). Computational modelling enables researchers to integrate several types of biological data into a single framework, to test perturbations in silico, and to predict
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the 1990s, continuing its theoretical development and application for ocean forecasting and climate predictions. NERSC leads DA development in the Norwegian Climate Prediction Model (NorCPM), which delivers
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for socially intelligent embodied agents. The central idea is to build predictive models that jointly represent people, the surrounding environment, and the actions of the embodied agent. Information from video
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, computation, and membrane characterization, the researcher will connect laboratory automation with predictive models and intelligent experiment selection to build a closed-loop discovery platform
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platforms, and will contribute to development of standardized, regulatory-aligned data frameworks to support predictive modeling and translational decision-making. Required Qualifications • Master’s degree
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, with machine-learned interatomic potentials fine-tuned on in-house DFT calculations. The aim is to identify the elementary steps that control catalytic activity, and to predict how it responds to changes
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therapies to patients. The group studies how retinal diseases progress over time, develops AI-based imaging tools and predictive models, and contributes to the development of new therapeutic strategies. Its