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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 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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About this role: This Knowledge Transfer Partnership, between Aston University and Genie UK Limited, offers an opportunity to oversee the development of an advanced AI-driven predictive and
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investigate CO₂ desublimation under controlled cryogenic conditions. The project is primarily experimental, supported by modelling and data analysis. You will design, build and operate a cryogenic experimental
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maintenance of command-line and web-based applications deploying machine learning (ML) models for chemical synthesis planning, property prediction, and molecular design and will work closely with faculty
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the design and operation of next-generation thermal systems, developing strategies to model, optimize, predict, and autonomously control thermal systems behavior in complex systems. The candidate will
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that reduces operational efficiency. Predicting where these processes will occur is a challenge because they are controlled in part by reservoir heterogeneity. This project will integrate core observations
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dynamically restricting, routing, and stabilizing trajectories through neural state space. We are interested in whether relatively low-dimensional inhibitory or feedback control signals can reorganize high
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, control and predict learning in real time. This project addresses a fundamental question in neuroscience and provides advanced training in technologies relevant to Parkinson’s disease and psychiatric
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tools, equipment, and processes (such as insulation testers, thermal imaging cameras and Thermal/Infrared (TIR) Predictive Testing procedures) to collect, perform initial diagnostics, and respond to data