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Funds are available for one year in the first instance.įurther details may be obtained from Dr Xuewu Dai, email apply for this vacancy please click 'Apply Now', and submit a covering letter, CV including research /education statements, and grants and publications list. The funds are available for a start date of the 1st February 2022 but some flexibility on this can be arranged. (7) Excellent interpersonal skills and the ability to communicate at all levels. (6) Written and spoken English of an excellent quality (5) Applied economic analysis and business model analysis skills.
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(4) Decision support digital tool and software development skills (3) Knowledge/Experience in electrical engineering, electrical vehicles and (smart) energy networks We particularly welcome candidates with an interest and strength in reinforcement learning, data science, job-shop scheduling, multi-stage decision-making process, and knowledge of Electric Vehicles and renewable energy, and experience in design and development of decision-support software. Experience in implementation of artificial intelligence (AI) and machine learning (ML) algorithms. (2) Computer programming skills with Python, Java or C++. (1) Mathematical modelling and multi-objective optimisation You should have experience with in the following two areas
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Candidates with highly relevant industry experience will also be considered.
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Having a Master or PhD degree in one of the above subject areas is desirable but not essential for this role. To be a successful applicant, you will have a First-Class BSc/BEng degree in Electrical and Electronic Engineering, Computer Science, Mathematics, Control Engineering, or similar discipline. The project ‘s ambitions are: (1) Modelling the spatial-temporal pattern and uncertainties of both EV demand and ORE supply (2) An optimised EV dispatching and charging schedule by developing a RL-based decision making (3) Development of a techno-economic analysis tool to evaluate emission reduction in urban environment. This is addressed by developing a data-driven Reinforcement Learning (RL) decision support tool that can provide a near real-time tactical dispatching and charging schedule to improve the self-consumption of ORES, thus reducing the actual emission of road transport.
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The purpose of this project is to answer the open question of how to improve the efficient use and self-consumption of highly variable ORES, whilst meeting the dynamic travel demands of collaborative EVs in fleets, at lowest possible investment and operation cost of charging stations and electricity storage facility and least impact on grid peak load. This post relates with an EPSRC/Siemens funded project “Electric Fleets with On-site Renewable Energy Sources” (EFORES), which studies the optimised near real-time dispatching and charging management of Electric Fleets using On-site Renewable Energy Sources (ORES). Applications are invited for a Research Assistant or Research Associate (PostDoc) to work on machine learning to optimize the dispatching and charging of Electric Fleets to improve the self-consumption from the On-site Renewable Energy Sources (ORES).