伦敦玛丽女王大学Dr. Xinwei Wang招收运筹学/机器学习/智能交通系统方向博士生（CSC）申请条件要求-申请方
主办方
伦敦大学玛丽王后学院
PhD直招介绍
研究方法：运筹学、机器学习、系统工程。
面向场景：智能交通系统。
课题1：Trustable Public Transit through Integrated Multimodal Transit Networks and Trip Planning
课题2：Towards Intelligent Decision Support and Risk Assessment Transport System
详情介绍请见以下英文描述。
【额外福利】
成功申请者将加入QMUL多学科研究团队，与QMUL-IOT2US实验室( https://iot.eecs.qmul.ac.uk/ ) , 英国领先的空管公司NTAS ( https://www.nats.aero )密切合作；
将有机会加入图灵研究院( https://www.turing.ac.uk/ )进行为期最长12个月的学习培训；
接受英国工程和自然科学研究委员会（EPSRC）Industrial Mobility and Doctoral Training Programme in Data Science and Engineering的课程培训；
【学校概况】
QMUL是英国伦敦大学四大核心学院之一，英国“常青藤联盟”罗素大学集团成员，英国科学与工程六校联盟成员，迄今共9位校友获诺贝尔奖。
USNEWS 2023：第100名；THE 2023：第124名；QS 2023：第125名。
在英国与CSC合作最久、支持力度最大的高校之一。
Project One: "Trustable Public Transit through Integrated Multimodal Transit Networks and Trip Planning" (Main advisor: Dr Jun Chen, Co-advisor: Dr Xinwei Wang)
As a recent study shows, longer travelling times using London underground and changing trains in busy interchange stations have a correlation with the spread of airborne infections such as COVID-19 and flu. Digital technology can promote active travel and ease overcrowding, reducing the risk of COVID-19. One of the digital tools for promoting active travel is the Rapid Cycleway Prioritisation Tool. This tool identifies and ranks locations for creating new cycleways using ‘cycling potential’ based on route distance and route hilliness. However, the ‘cycling potential’ currently doesn’t take into account how the cycleways integrate into the mass transit network and how they could enable the travellers to avoid crowded parts. Existing multi-modal trip planning tools combine different modes of transport for trip recommendations (e.g. Google Maps, Swifly, moovit, OpenTripPlanner, Tfl app). However, none or only limited crowdedness information is available. The problem of finding the best route with multiple objectives (e.g. travel time, crowdedness, number of changes, etc.) is NP hard (i.e. the complexity of solving the problem grows exponentially with the size of the problem). Furthermore, combining multiple modes of transport and integrating real-time crowdedness feeds while requiring fast response times makes this problem computationally challenging, which are currently absent in the existing trip planners. In order to address the abovementioned gaps in the current trip planning tools, the following questions (Q) will be answered in this project.
Q1: How to detect locations with increased congestion and crowding in real-time?
Q2: How to inform travellers to postpone their trips to less crowded periods or redirect them to other modes of transport?
Q3: How to assist service providers in managing overcrowding and inform them of the available options?
Q4: How to integrate different forms of travel to enable a seamless navigation for travellers, and help transport authorities prioritise new infrastructure?
Project Two: "Towards Intelligent Decision Support and Risk Assessment Transport System" (Main advisor: Dr Xinwei Wang, Co-advisor: Dr Jun Chen)
Future traffic management systems are of great importance to better utilise the existing transport infrastructure, ensure traffic safety and reduce pollution. However, existing transport planning approaches could not support real-time operations due to frequent traffic delays and disruptions, and are incapable to consider subsequent useful traffic information.
This project aims to develop a real-time decision support transport system for a novel hierarchical rolling horizon structure, maintaining safety and performance in a dynamically changing environment. The framework will deploy reactive scheduling procedures to revise the baseline solution for real-time decision making. Within the framework, a proactive risk modelling approach will also be developed to quantify the likelihood of certain incidents to be transformed into accidents, and estimate the corresponding risk tolerance level, eventually developing new safety barriers.
About the supervisors:
• Dr Jun Chen ( https://www.sems.qmul.ac.uk/staff/jun.chen ) is a Reader in Intelligent Systems Engineering at QMUL. He has published more than 70 scientific papers in areas of multi-objective optimisation, interpretable fuzzy systems, data-driven modelling, and intelligent transportation systems. Dr Chen was among the first researchers to investigate the trade-off between taxi time and fuel consumption in airport ground movements (EP/H004424/2), and proposed the Active Routing (AR) concept. AR forms the cornerstone of a major ongoing EPSRC funded project (EP/N029496/1, EP/N029356/1 and EP/N029577/1, in total in excess of £1M) for which Dr Chen is the lead PI (with BAEs, AirFrance-KLM, Rolls Royce, Manchester and Zurich Airports, and Simio plc.). He has also been the PI on EPSRC-QMUL IAA projects, four industrial projects with Anglian Water, and was the CI on three Innovate UK projects (with IMS and Tesco plc, and Siemens). He serves as a full member of the EPSRC Peer Review College. He is also a Turing Fellow at Alan Turing Institute. Dr Chen is working closely with industries and regulatory bodies to produce guidance on safe, secure and successful adoption of AI technologies in Aeronautical systems, in particular for ground-based systems. Following the above best practice and taking a systems approach, the ground-based decision support system developed within Dr Chen's group aligned to these development methodologies as early in the development as possible to ensure smooth transition to operational deployment in future.
PDF版文件请见： https://www.dropbox.com/s/cltkngwhne1aqxv/CSC-QMUL-2023.pdf?dl=0#opennewwindow
伦敦大学玛丽王后学院 Dr. Xinwei Wang招收运筹学/机器学习/智能交通系统方向博士生（CSC）项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
About the scholarship:
• CSC will provide living expenses (£1350 per month 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 at QMUL and work closely with leading industrial partners ( https://iot.eecs.qmul.ac.uk/ , https://www.turing.ac.uk/ ) in the UK;
• 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;
• The student will also benefit from training in Data Science and Research provided by the EPSRC Industrial Mobility and Doctoral Training Programme in Data Science and Engineering;
• Additional funding will be available to cover site visits and dissemination of the results at international conferences, workshops and collaborative universities and industrial partners.
For more information on how to apply for this QMUL-CSC scholarship, please refer to this link:
https://www.qmul.ac.uk/scholarships/items/china-scholarship-council-scholarships.html#opennewwindow
伦敦大学玛丽王后学院Phd申请条件和要求都有哪些？Dr. Xinwei Wang招收运筹学/机器学习/智能交通系统方向博士生（CSC）项目是不是全奖？有没有奖学金？下面我们一起看一下伦敦大学玛丽王后学院申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
About the applicants:
• A top Master or undergraduate student (top 5%) in the following areas: Operational Research, Machine Learning, Energy Consumption Modelling, Transportation Engineering, Systems 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.
报名方式
招生人信息
Dr. Xinwei Wang
Dr Xinwei Wang is a Lecturer (Assistant Professor) at Queen Mary University of London (QMUL), UK. He was a Postdoc at TU Delft, The Netherlands from 2020 to 2022. Prior to that, he was a Postdoc at QMUL from 2019 to 2020, and he obtained a PhD degree from Beihang University, China in 2019. Over the years, he has integrated artificial intelligence and systems engineering for risk assessment, motion planning and decision making in aerospace/transport intelligent systems. He has established close connections to world-leading research groups from University of Sheffield, Cranfield University, Loughborough University, TU Berlin, TU Delft, TU Dresden, EPFL and also top universities in China. He is a recipient of Marie Sklodowska-Curie Actions Co-Fund Fellowship (2022), and IEEE ITSS Young Professionals Travelling Fellowship (2022). He has authored over 30 papers, including those in TR Part C, IEEE T-ITS, IEEE T-FS, IEEE T-SMC, IEEE T-VT, IEEE T-AES, etc.
邮箱：xinwei.wang@qmul.ac.uk
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