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Field
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(AI) & Deep Reinforcement Learning (DRL) for energy optimization ✔ Predictive Maintenance & Failure Analysis using Machine Learning and Physics-Based Modelling ✔ Data Science & Advanced Analytics with
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biosynthetic machinery Applying novel GAG analytical technologies to investigate how changes in GAG structure and organisation drive and/or respond to shifting developmental stages. Working Environment: Based
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of heterogeneous catalysis General knowledge of homogenous catalysis Experience in reactor operations for homogenous and/or heterogenous catalytic systems Experience in solvent handling and gas and liquid analytical
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-assisted design) and state-of-the-art experimental techniques (neutron scattering, data analysis, sample fabrication). The successful candidate will engage in cutting-edge research at the interface of theory
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees
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upper second-class degree in Chemistry, Biochemistry, microbiology or a relevant natural sciences-related subject. Good analytical, problem-solving and MS Excel skills. Ability to work collaboratively and
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relevant to the proposed PhD project. Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication skills are essential
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, deposition and characterisation tools, including bespoke burner-rig testing design for realistic thermal testing. Combination of experimental, analytical, and modelling training, ideal for interdisciplinary