Current PhD Opportunities

As part of our vibrant community of PhD students, you will make a direct contribution to the world-class research of the Graduate School, and develop the skills you need to enjoy a rewarding career.

Whatever your field of interest in relation to the environment – whether in the natural or social sciences – the size and scope of the Graduate School for the Environment means that you are guaranteed to find a suitably stimulating research project to work on.

Support structure

As a PhD student, you will immediately become a valued member of a research group, each of which is headed up by one of our internationally respected academics and supported by an array of post-doctoral research associates and technicians.

You will make your personal contribution to the group – and be taught the research skills you need – under the guidance of one or more supervisors specifically chosen to suit your area of interest. It is with the support of your supervisors and your research group colleagues that you will be able to extract full value from your time at the Graduate School.

Current PhD Opportunities

  • Use of Machine Learning for Regional and Country Wealth Prediction from Satellite imagery

    Supervisors: Pete Atkinson, James Lawrence

    Deadline for applications: 30 September 2018

    Studentship funding: Full studentships (UK/EU tuition fees and stipend (£14,777 2018/19 [tax free])) for UK/EU students for 3.5 years. Unfortunately funding is not available for International (non-EU) students. 

    Why is this project interesting?

    In recent years the number of satellites specialising in capturing imagery of the Earth’s surface has risen dramatically, and increased investment from both ESA and commercial satellite operators are likely to continue this trend over the next decade. With every point on the Earth’s surface captured multiple times per day there is now a huge opportunity to understand the world’s economies in more detail. Traditional techniques used throughout the remote sensing sector are no longer able to effectively handle the scale of data now available. A new approach based around machine learning is required to interrogate these data in a timely fashion.

    This PhD will present the successful candidate with an excellent opportunity to develop world-leading capability in the use of machine learning techniques for imagery, video and trend analysis. The candidate will need to develop techniques to identify and understand the changes in key economic indicators such as mine output, urban growth, commodity extraction and storage and shipping.

    The PhD will be supported by machine learning experts, physicists and data scientists at Geospatial Insight.

    What’s in it for you?

    Become expert in the application of machine learning for the analysis of satellite borne imagery. The Earth observation (EO) sector is in a phase of growth as businesses are beginning to understand the value of EO data, and machine learning approaches are reducing costs, making the business proposition viable. Thus, this PhD provides skills and expertise in a sector where demand for such skills is high. 

    Develop links with external organisations. This project benefits from linkages with Geospatial Insight, one of the UK’s leading technology businesses for the development of downstream imagery processing services and products.

    Join an exciting research environment. You will benefit from the research training programmes offered by the Faculty of Science and Technology at Lancaster University, by being part of the large and vibrant Lancaster Environment Centre and by becoming a member of the Geospatial Data Science research group. This project is at the cutting edge of what is possible using EO data and there is great potential for high quality academic publication of the results.

    Who should apply?

    We are seeking applications from graduates with a good (i.e., 1st class or 2.1) Undergraduate degree, and preferably also a Masters degree, in a Machine Learning-related subject. You should have a strong background in computer science, mathematics, physical science or geography/environmental science with strong quantitative (e.g., programming) skills. You must have demonstrable potential for creative, high-quality PhD research.

    How to apply

    Please download the Use of Machine Learning for Regional and Country Wealth Prediction information for the application process. 

How to Apply

Launch your career in research with a funded PhD. Please read carefully the advertised project information, including the funding eligibility as only applicants who have a relevant background and meet the funding criteria can will be considered.

  • Download the Application Form and Reference Form.
  • Complete the Application Form, renaming the document with your 'Name and Application Form' e.g., Joe Bloggs' Application Form.
  • Send the completed Application Form and a CV to the email address as indicated within the project advert.
    Project adverts are available under Current Opportunities.
    Applications and CVs must be submitted as either Word documents or pdf files - no other file types are accepted.
  • Rename the referee form with your 'Name and Reference', e.g., Joe Bloggs Reference.
    Send the renamed reference form to two referees and request them to forward the referee document to the email address as indicated in the project advert.
    References must be submitted as either Word documents or pdf files - no other file types are accepted.
    It is important that you ensure references are submitted by the closing date or as soon as possible.
  • You will receive an acknowledgment in receipt of successfully sending the application documents.

Funded PhDs are advertised throughout the year, however the majority of projects are advertised between December and May for an October start. In some circumstances, dependent on the funding, start dates in January and April will be considered.

  • Please note that only applications submitted as per these instructions will be considered.
  • If English is not your first language, you will be required to provide evidence of your proficiency in English. This evidence is only required if you are offered a funded PhD, and is not required as part of this application process.
  • If you do not hear from us within four weeks of the closing date then you have been unsuccessful on this occasion. If you would like feedback on your application, please contact the supervisors of the project.

Research Groups

Research training

We take care of all of our students at Lancaster University. The Faculty of Science and Technology runs a series of training sessions designed to improve your skills and abilities during your PhD.

Learn more