英国伯明翰大学、伦敦大学学院博士招生项目推荐

本期 “博士招生”为大家推荐英国伯明翰大学、伦敦大学学院博士招生项目。

1、University of Birmingham伯明翰大学

伯明翰大学（University of Birmingham），世界百强名校，始建于1825年，坐落于英国第二大城市伯明翰，英国”红砖大学“之一。

伯明翰大学在2023U.S. News世界大学排名第89名，2023QS世界大学排名第91名。在2017年英国官方组织的教学卓越框架（TEF）评估中获得金奖。根据QS世界大学毕业生的就业能力排名中，伯明翰大学排在111-120位。

Fusing Simulation with Data Science

1► Dr Xiaocheng Shang

2► Friday, July 21, 2023

3► Competition Funded PhD Project

About the Project

Open PhD Opportunities in Weather and Climate

The Opportunity

The University of Birmingham has recently been announced as new academic partner of the UK Met Office. As part of the partnership, we are offering a small number of fully-funded studentships to work on co-created projects in relevant areas across the two organisations. The areas are:

· Advancing Observations: Working towards a step change in observational capabilities, realising the value of third party and opportunistic observations to address growing needs.

· Fusing Simulation with Data Science: Exploring the use of new and evolving data science methods such as artificial intelligence, machine learning and advanced data assimilation for weather and climate prediction and impact-based services

· Hazard to Decision Making: Using an interdisciplinary approach to increase understanding of the impacts of hazards and to develop better impact-based services.

· Capturing Environmental Complexity: Extend applied environmental prediction capabilities with a focus on cities, air quality, the water cycle, and carbon and nitrogen cycles.

These are aligned to the latest Met Office research and innovation strategy. More details on each area can be found here:

https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/approach/r-i_strategy_full_version_v2.pdf

How to Apply

We are looking for applicants across these four areas with a willingness to flexibly shape a PhD to start in Autumn 2023. Applicants are expected to include a 1000 word (max.) research proposal focussed on one of the four above areas and aligned to the Met Office research and innovation strategy as part of their application. This will form the basis for further discussion and will be used to assign a supervisory team from the University and the Met Office. The proposal will then evolve via a co-creation process to ensure that it is mutually beneficial to all. Please note, that during this process the proposal could change considerably and therefore, a willingness to work more generally within the chosen area is an essential requirement.

Please apply through the following link, choosing the discipline most relevant to yourself:

· Maths

· Civil Engineering

· Environmental Science (Department of Environmental Health and Risk Management)

https://www.birmingham.ac.uk/research/moap/studentship-opportunities.aspx

The closing date is the 21st July 2023.

Eligibility

Applications are welcome from all with a good degree from relevant disciplines. Please note that the nature of the opportunity limits eligibility to UK students only.

Please direct any enquiries to Professor Lee Chapman (l.chapman@bham.ac.uk) or Professor Gregor Leckebusch (g.c.leckebusch@bham.ac.uk)

Funding Notes

Applications are welcome from all with a good degree from relevant disciplines. Please note that the nature of the opportunity limits eligibility to UK students only.

2、University College London伦敦大学学院

伦敦大学学院（University College London，简称：UCL ），是一所公立研究型大学，为伦敦大学联盟的创校学院、罗素大学集团和欧洲研究型大学联盟创始成员，被誉为金三角名校和“G5超级精英大学”之一 。

2021~22年度，UCL位居QS世界大学排名第8 ，U.S. News世界大学排名第16，泰晤士高等教育世界大学排名第16，软科世界大学学术排名第17。

PhD - Conversational Systems for Complex Routing Problems in Open Spaces

1► Mr J Haworth

2► Friday, July 07, 2023

3► Competition Funded PhD Project

About the Project

Conversational Systems for Complex Routing Problems in Open Spaces

UCL’s SpaceTimeLab, Web Intelligence Group and Ordnance Survey (OS) would like to invite applications for a 4-year PhD studentship to develop conversational systems to facilitate routing across open spaces.

Route planning in urban transportation networks is widely researched, with numerous commercial and open-source solutions. However, the problem of routing across open spaces where networks are not clearly defined is less well-researched. In particular, open access land (such as common land and some national parks) can be roamed freely, allowing freedom in route choice. However, this freedom is typically constrained by the characteristics of the terrain (such as vegetation type, gradient, and presence of existing paths) and the user (experience, fitness level, equipment, mobility requirements, origin and destination, duration/distance of route). This leads to a complex multi-criteria routing problem. This research will develop a conversational system combining terrain characteristics and user requirements for route planning solutions across open areas. The system will combine user requirements with OS data to generate initial routes. It will then interact with the user throughout the journey to enable dynamic rerouting based on changing requirements.

Key Research Questions:

· To what extent can data from Ordnance Survey and other sources provide routes across open access land that are safe and tailored to user requirements? In open access land, the safety of provided routes is important in order to avoid potential risks. Therefore, the quality, completeness and granularity of the input data is very important. However, data on open land often lacks the spatial and temporal granularity of urban datasets. This part of the research will assess the data available from Ordnance Survey, such as the Detailed Path Network, to understand it’s strengths and limitations in routing. Additional data sources that may support the project will be reviewed.

· What are the challenges with embedding geospatial data query into conversational systems? Geospatial query with conversational systems requires interpreting terms that refer to

geographical features or operators. For example a user may ask to be directed to a ‘good view’, a ‘lake’ or a ‘picnic area’. Similarly, they may ask questions based on proximity such as ‘nearby’, ‘nearest’, ‘close’, ‘within half an hour’ etc. These questions, which may also vary according to regional dialects, must be translated into geospatial queries to generate and update routes. How this vocabulary is interpreted will change the results. For example, how does one define ‘nearby’ and how does this vary according to an individual’s preferences; how can a ‘good view’ be extracted from geospatial data? What other data may be used to augment such queries? Furthermore, how can a system understand and interpret the many different terms people may use for the same query, such as ‘near’, ‘close’, ‘convenient’.

· What are the challenges of developing a dynamic routing system to be used in remote environments where mobile coverage may be limited?

For a routing solution to be practical in open areas, it must be able to operate with limited or no mobile data availability. This part of the project will explore methods for doing this, such as caching of geospatial data and embedded algorithm design.

Person Specification

You should possess a strong bachelor’s degree (1st Class or 2:1 minimum) or Masters Degree in Computer Science, Machine Learning, Geospatial Science, Engineering or a related discipline. Strong programming skills are essential, with Python the preferred language. Experience of software development is desirable.

As well as regular individual supervision, the student will be able to attend relevant modules on UCL’s MSc Spatio-temporal Analytics and Big Data Mining degree or other selected modules within UCL. They will have the opportunity to embed their work within the OS through a series of extended visits and will participate in the OS’s PhD researcher programme. They will also benefit from working in a large team of research students in UCL’s SpaceTimeLab and Web Intelligence Group.

Candidates will ideally have some relevant previous research experience and should also have excellent communication and presentation skills.

Supervisors

The UCL supervisory team for this position consists of Dr James Haworth, Associate Professor in Spatio-temporal Analytics, and Dr Aldo Lipani, Lecturer in Machine Learning, in SpaceTimeLab in the Department of Civil, Environmental & Geomatic Engineering. The Ordnance Survey supervisor will be Dr Stefano Cavazzi, Principal Innovation and Research Scientist. The candidate will be based at the Department for Civil, Environmental & Geomatic Engineering but will also visit Ordnance Survey offices at regular intervals and have the opportunity to work with the Web Intelligence Group based in UCL Computer Science.

How To Apply

Applicants should apply through https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/civil-environmental-and-geomatic-engineering-mphil-phd

Select the 2023-2024 full time option, and follow the instructions to complete the information required. In order to be considered for this post, you must upload:

· A CV – (under the ‘employment header) · A supporting statement following the guidance here: https://www.ucl.ac.uk/prospective-students/graduate/writing-your-personal-statement

· (You are not required to write a proposal)

If you have any questions regarding the position, please reach out to Dr Claire Ellul

Contact name

Dr James Haworth

Contact details

j.haworth@ucl.ac.uk

Closing date

7th July 2023

Interview date

TBC (w/c 10th July)

Studentship start date

25 September 2023