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2D and 3D crowd analysis and simulation using deep learning and big data analysis

PGR-P-88

Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
Country eligibility
International (open to all nationalities, including the UK)
Funding
Non-funded
Supervisors
Dr He Wang
Schools
School of Computing
<h2 class="heading hide-accessible">Summary</h2>

Every future city will have a digital `twin' that consumes data from the physical city and generates predictions for its design, construction, and management. The centre-piece of this digital twin is its residents. This research project is to build such residents, a `digital twin' of crowds, by proposing the next-generation mathematical models and artificial intelligence algorithms. This will be achieved by combining machine learning with neuroscience, architectural design and crowd management. Today, it is expected that more than 6.7 billion people will aggregate in urban spaces by 2050, leading to megacities of 10 million inhabitants (United Nations). The research project will lead to a crowd-driven framework that can predict crowd motions, help design new spaces and improve existing spaces, to eliminate potential dangers, minimise discomfort and maximise efficiency, enabling planners and policymakers to meet the great challenges of fast urbanisation in the 21st century. This project is to look into the fundamental crowd motions in different environments including indoor/outdoor scenarios. The goal of this project is to propose a series of new models and mathematical frameworks to capture the crowd motions for the purposes of analysis and simulation. The research falls into the category of data-driven crowd analysis where data is intensively used for analysis as compared to traditional empirical modelling where concise mathematical models are made trying to capture the complex structure in the data. However, because of the high complexity, more data (big data) is needed and meanwhile corresponding algorithms and models with enough capacity for data consumption are to be developed. The project is currently accepting PhD applications every year.

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="http://www.leeds.ac.uk/rsa/prospective_students/apply/I_want_to_apply.html">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is 2D and 3D crowd analysis and simulation using deep learning and big data analysis as well as&nbsp;<a href="Dr He Wang">Dr He Wang</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p><strong>Self-Funding Students</strong></p> <p><strong>Funding Eligibility</strong></p> <p><strong>UK/EU</strong> &ndash;&nbsp;Leeds Doctoral Scholarship Award paying Academic Fees and Maintenance matching EPSRC rate of &pound;15,285&nbsp;per year for 3 years.&nbsp; Alumni Bursary is available for previous graduates from the University of Leeds offering 10% discount on Academic Fees only.</p> <p><strong>International Students</strong> &ndash;&nbsp;China Scholarship Council-University of Leeds Scholarship Award paying Academic Fees for 3 years, Commonwealth PhD and Commonwealth Split-Site Scholarships for Low and Middle Income countries.&nbsp; Alumni Bursary is available for previous graduates from the University of Leeds offering 10% discount on Academic Fees only.</p>

<h2 class="heading">Contact details</h2>

<p>For further information regard your application, please contact Doctoral College Admissions by&nbsp;email:&nbsp;<a href="mailto:EMAIL@leeds.ac.uk">p</a><a href="mailto:phd@engineering.leeds.ac.uk">hd@engineering.leeds.ac.uk</a> or by&nbsp;telephone: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr He Wang by email:&nbsp;&nbsp;<a href="mailto:H.E.Wang@leeds.ac.uk">H.E.Wang@leeds.ac.uk</a></p>


<h3 class="heading heading--sm">Linked funding opportunities</h3>
<h3 class="heading heading--sm">Linked research areas</h3>