倫敦大學(xué)瑪麗皇后學(xué)院（QMUL）Dr Jun Chen課題組誠招2021年CSC公派博士生1名，QMUL提供4年的學(xué)費，CSC提供生活費。導(dǎo)師人很好，學(xué)術(shù)水平精湛，歡迎交通、優(yōu)化、機(jī)器學(xué)習(xí)、控制方向的優(yōu)秀本科和碩士畢業(yè)生報名。申請截止時間為2021年1月27日，有意向的同學(xué)請先盡快與Dr Jun Chen聯(lián)系（Email：jun.chen@qmul.ac.uk），有問題也可私信我~~

具體招生信息如下：

A CSC studentship (2021) is available now:

Project Title: "Integrated Airport Airside Simulation, Control & Decision-making"

A CSC studentship for one Chinese student is available in areas of Operational Research, Machine Learning and Combinatorial Optimisation at the Division of Aerospace Engineering, School of Engineering and Material Sciences (SEMS), Queen Mary University of London (QMUL).
The Active Routing framework has been developed within the on-going EPSRC project (TRANSIT: Towards a robust airport decision support system for intelligent taxiing, www.transitproject.co.uk) led by Dr Jun Chen[1-5]. TRANSIT has led to three ongoing EPSRC Impact Acceleration Account (IAA) projects to develop a simulation platform to support Trajectory-based Taxi operations.

The proposed research will be set within this well-structured UK research environment and run initially in conjunction with TRANSIT and two EPSRC IAA projects. The project will capitalise on the real-world problem instances through the TRANSIT and IAA projects to integrate the simulation platform for ground movement with en-route air traffic control and employ artificial intelligence techniques (e.g. machine learning and fuzzy logic) for strategic and tactical decision making. The integrated environment and artificial intelligence methods will leverage the simulation platform capabilities to provide simulated performance indicators for intelligent airport decision making and enable the search for the best air traffic control strategies. Two major challenges will be addressed. (1) Modelling of air traffic controller strategies in air and on ground for current and future trajectory based operational concepts[6,7]. (2) Prediction and resolution of ground movement conflicts[8,9].
•        CSC will provide living expenses (up to 4 years) and one return flight ticket and QMUL will provide a full tuition fee;

•        Successful applicants will join the multi-disciplinary research team of TRANSIT and has chances to interact with world-renowned industrial partners e.g. BAE Systems, AVISU Ltd, Air France KLM, Rolls Royce, Manchester Airport, Zurich Airport and Simio;

•        The student will have the opportunity to join the Alan Turing Institute (https://www.turing.ac.uk/) for up to 12 months to boost their skills, grow their network and work alongside other Turing researchers;

•        Additional funding will be available to cover site visits and dissemination of the results at international conferences, workshops and collaborative universities (e.g. NUAA, BUAA and NJFU in China) and industrial partners.

About the university:

•        110th in the world and 12th in the UK (Times Higher Education World Rankings) and 126th in the World (QS World University Rankings)

•        9th in the last UK Research Excellence Framework (REF-2014)

•        A member of the Russell Group of leading UK universities

•        Top 10 in the UK for highest graduate starting salaries

•        9 Nobel Prize winners

About the applicants:

•        A top Master or undergraduate student (top 5%) in at least two of the following areas: Operational Research, Machine Learning, Combinatorial Optimisation, transportation engineering and control Engineering.

•        English minimum requirement: IELTS 6.5 or equivalent English tests.

•        Preferably from a top Chinese university (211 and 985).

•        Preferably with some decent publications.

The deadline for submitting your application to QMUL is 27 January 2021. If you are interested in this studentship, please contact Dr Jun Chen as soon as possible by email: jun.chen@qmul.ac.uk.

For more information on how to apply for this QMUL-CSC scholarship, please refer to this link:

https://www.sems.qmul.ac.uk/rese ... ip-council-csc-phd-

studentships-for-entry-in-september-2021
Reference:

[1]        Chen, J., Weiszer, M., Locatelli, G., Ravizza, S., Atkin, J. A., Stewart, P., & Burke, E. K. (2016). Toward a more realistic, cost-effective, and greener ground movement through active routing: A multiobjective shortest path approach. IEEE Transactions on Intelligent Transportation Systems, 17(12), 3524-3540.

[2]        Brownlee, A. E., Weiszer, M., Chen, J., Ravizza, S., Woodward, J. R., & Burke, E. K. (2018). A fuzzy approach to addressing uncertainty in Airport Ground Movement optimisation. Transportation Research Part C: Emerging Technologies, 92, 150-175.

[3]        Weiszer, M., Chen, J., Stewart, P., & Zhang, X. (2018). Preference-based evolutionary algorithm for airport surface operations. Transportation Research Part C: Emerging Technologies, 91, 296-316.

[4]        Zhang, T., Ding, M., Zuo, H., Chen, J., Weiszer, M., Qian, X., & Burke, E. K. (2018). An online speed profile generation approach for efficient airport ground movement. Transportation Research Part C: Emerging Technologies, 93, 256-272.

[5]        Xinwei Wang, Alexander Brownlee, John Woodward, Michal Weiszer, Mahdi Mahfouf, Jun Chen. “Aircraft taxi time prediction: Feature importance and their implications." Transportation Research Part C: Emerging Technologies (accepted).

[6]        https://homepage.tudelft.nl/7p97s/BlueSky/

[7]        Cao X, Zhu X, Tian Z, Chen J, Wu D and Du W (2018). "A knowledge-transfer-based learning framework for airspace operation complexity evaluation." Transportation Research Part C: Emerging Technologies vol. 95, 61-81.

[8]        Liu H, Liu F, Zhang X, Guan X, Chen J and Savinaud P (2018). "Aircraft conflict resolution method based on hybrid ant colony optimization and artificial potential field." Science China Information Sciences vol. 61, (12).

[9]        Guan X, Zhang X, Lv R, Chen J and Michal W (2017). "A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation." Science China Information Sciences vol. 60, (11).

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