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Field
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expertise in nonlinear model predictive control. Expertise in numerical optimal control. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently
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(i.e. relationally interdependent systems) and encoding nonlinearities in these. The group has plentiful in-house simulation capabilities of numerical models and access to extensive real-world monitoring
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Infrastructure? No Offer Description Start date/duration 01.11.2025 for 4 years Job profile: Your tasks: Dissertation in the field of theoretical quantum optics, theoretical modelling of open many-body quantum
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Infrastructure? No Offer Description Start date/duration 01.11.2025 for 4 years Job profile: Your tasks: Dissertation in the field of theoretical quantum optics, theoretical modelling of open many-body quantum
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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model predictive control. Expertise in numerical optimal control. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently and in a structured
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Genetics, Reproduction and Development (GRAD) Large Research Group to: Use single-cell omics techniques to analyze human pluripotent stem cells, human embryos, and stem cell-derived embryo models. Study how
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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design