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of this project, please contact Prof. Tony McNally (T.McNally@warwick.ac.uk ) directly for further information. Essential and Desirable Criteria: 1 or 2.1 degree in Materials Science & Engineering, Chemical
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diverse backgrounds (e.g., economics, computer science, information systems, engineering, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities
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For specific information about the PhD fellowship, please contact the principal supervisor. [General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https
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printing tests using the in-house developed data acquisition system as an in-situ monitoring solution. Validate and refine the system to improve data capture. Investigate feature engineering techniques
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and Computer Science (EEMCS) uses mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology (ICT). With ICT present in almost every
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information about the faculty of Science at https://www.universiteitleiden.nl/en/science/about-faculty-of-science . For more information about working at Leiden university please visit https
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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did