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PhD Studentship on Artificial Intelligence for Railway Operations and Management

PGR-P-504

Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
Country eligibility
UK and EU
Funding
Funded
Source of funding
Other
Supervisors
Professor Ronghui Liu
Additional supervisors
Dr Zhiyuan Lin
Schools
Institute for Transport Studies
<h2 class="heading hide-accessible">Summary</h2>

Defining the roadmaps for Artificial Intelligence applications for railway operations and network management Applications are invited for a PhD studentship in innovative approaches in artificial intelligence for railway scheduling and operations, to be based in Institute for Transport Studies at University of Leeds. The position is an opportunity to combine cutting-edge research at the intersection of railway scheduling and artificial intelligence techniques such as machine learning, neural networks. The overall objective of the PhD research project is to investigate the potential of Artificial Intelligence (AI) in the rail sector and contribute to the definition of roadmaps for future research in operational intelligence and network management. In particular, the student will develop and compare different AI approaches, e.g. machine learning, deep and reinforcement learning, for railway traffic planning and management. He or she will have a chance to investigate using AI for solving combinatorial optimization problems, AI for supporting optimization models, with special focus on the optimization models for railway operations and management. This PhD research forms part of the RAILS project, funded by 'Shift2Rail Joint Undertaking, a body of the European Union' and in collaboration with other European universities from Italy, Sweden and the Netherlands. The student will have the opportunity to work with the academic leads from these institutions, including spending up to one year at Digital Rail Traffic Lab at TU Delft (www.tudelft.nl/drtlab). The project is suitable for a student with a top MSc (preferable) or first-class bachelor&rsquo;s degree in artificial intelligence, transport, computer science, operation research (optimisation), mathematics, physics, or a subject of highly quantitative nature. Previous coursework or experiences in at least one of the above areas is necessary. A strong programming background will be essential for this project. Prior experiences in one or more of the following areas is desirable but not mandatory: supervised machine learning in transport prediction, transport timetabling/scheduling, and applying artificial intelligence in theoretical or real-world combinatorial optimisation problems. The scholarship is available for UK/EU students and consists of tuition fee and an annual stipend for three years. To apply for the scholarship, please visit https://environment.leeds.ac.uk/transport-research-degrees/doc/apply-2. For informal enquiries about the position, please contact Professor Ronghui Liu (R.Liu@its.leeds.ac.uk) or Dr Zhiyuan Lin (Z.Liu@leeds.ac.uk) with a short summary of your background and research interests in the technical themes mentioned above.

<h2 class="heading hide-accessible">Full description</h2>

<p>Defining the roadmaps for Artificial Intelligence applications for railway operations and network management&nbsp;&nbsp;</p> <form> <p>Applications are invited for a PhD studentship in innovative approaches in artificial intelligence for railway scheduling and operations, to be based in Institute for Transport Studies at University of Leeds. The position is an opportunity to combine cutting-edge research at the intersection of railway scheduling and artificial intelligence techniques such as machine learning, neural networks.</p> <p>The overall objective of the PhD research project is to investigate the potential of Artificial Intelligence (AI) in the rail sector and contribute to the definition of roadmaps for future research in operational intelligence and network management. In particular, the student will develop and compare different AI approaches, e.g. machine learning, deep and reinforcement learning, for railway traffic planning and management. He or she will have a chance to investigate using AI for solving combinatorial optimization problems, AI for supporting optimization models, with special focus on the optimization models for railway operations and management.</p> <p>This PhD research forms part of the RAILS project, funded by &lsquo;Shift2Rail Joint Undertaking, a body of the European Union&rsquo; and in collaboration with other European universities from Italy, Sweden and the Netherlands. The student will have the opportunity to work with the academic leads from these institutions, including spending up to one year at Digital Rail Traffic Lab at TU Delft (<a href="http://www.tudelft.nl/drtlab/">www.tudelft.nl/drtlab</a>).</p> <p><strong>Desired Background</strong></p> <p>The project is suitable for a student with a top MSc (preferable) or first-class&nbsp;bachelor&rsquo;s degree in artificial intelligence, transport, computer science, operation research (optimisation), mathematics, physics, or a subject of highly quantitative nature.</p> <p>Previous coursework or experiences in at least one of the above areas is necessary. A strong programming background will be essential for this&nbsp;project. Prior experiences in one or more of the following areas is desirable but not mandatory: supervised machine learning in transport prediction, transport timetabling/scheduling, and applying artificial intelligence in theoretical or real-world combinatorial optimisation problems.</p> <p><strong>Why ITS and Leeds</strong></p> <p>The Institute for Transport Studies is one of the UK&rsquo;s leading departments for transport teaching and research. We deliver internationally excellent research outputs, which impact upon transport policy and practice, and contribute to the wider economy and society. Our research feeds directly into our teaching, which means you&rsquo;ll learn about the latest developments in your field from world-leading researchers.</p> <p><strong>University of Leeds, established in 1904, is one of the largest higher education institutions in the UK. We are renowned globally for the quality of our teaching and research. The strength of our academic expertise combined with the breadth of disciplines we cover, provides a wealth of opportunities and has real impact on the world in cultural, economic and societal ways. The University strives to achieve academic excellence within an ethical framework informed by our values of integrity, equality and inclusion, community and professionalism.&nbsp; </strong></p> <p>&nbsp;</p> </form>

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

<p>Formal PhD applications for research degree study should be made&nbsp;to the Institute&nbsp;for Transport Studies 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 &ldquo;PhD Studentship on Artificial Intelligence for Railway Operations and Management&rdquo; as well as Prof. Ronghui Liu and Dr Zhiyuan Lin&nbsp;as your proposed supervisors.</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. Desired Background: The project is suitable for a student with a top MSc (preferable) or first-class bachelor&rsquo;s degree in artificial intelligence, transport, computer science, operation research (optimisation), mathematics, physics, or a subject of highly quantitative nature. Previous coursework or experiences in at least one of the above areas is necessary. A strong programming background will be essential for this project. Prior experiences in one or more of the following areas is desirable but not mandatory: supervised machine learning in transport prediction, transport timetabling/scheduling, and applying artificial intelligence in theoretical or real-world combinatorial optimisation problems.

<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>The scholarship is available for UK/EU students and consists of tuition fee and an annual stipend for three years.</p>

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

<p>For informal enquiries about the position, please contact Professor Ronghui Liu (R.Liu@its.leeds.ac.uk)&nbsp;or Dr Zhiyuan Lin (<a href="mailto:Z.Liu@leeds.ac.uk">Z.Liu@leeds.ac.uk</a>) with a short summary of your background and research interests in the technical themes mentioned above.</p> <p>Any enquiries about the application procedure can be sent to <a href="mailto:ENV-PGR@leeds.ac.uk">ENV-PGR@leeds.ac.uk</a> which will come to this Graduate School Office Admissions Officer.</p>