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

  • Data-smart ecosystem models

    Supervisors: Dr. Jess Davies and Prof. Gordon Blair

    Deadline for applications: Midnight 1 July

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

    Why is this project interesting?

    We are in the midst of an environmental data revolution. Advances in sensing technologies, open data, and data mining are rapidly increasing the volume and diversity of data we have on our natural world. How best to make use of this data is a growing challenge. This studentship will explore the potential for creating a new generation of data-smart terrestrial ecosystem models that are primed to exploit this environmental data revolution. You will explore how advances in data science, such as machine learning, can be combined with more traditional process-based ecosystem modelling approaches to provide new insights into ecosystem processes and improve our abilities to predict and sustainably manage their future.

    What’s in it for you?

    This is an exciting opportunity to work at the interface between data science and environmental sciences and help mobilise data science in the pursuit of a sustainable world. In this studentship, you will:

    • Gain a highly valuable skillset in data science and environmental modelling.
    • Be supported by a supervisory team who offer international leadership in data science and plant-soil ecosystem modelling, and who both have deep experience of cross-disciplinary approaches to environmental challenges.
    • Join a diverse community of data scientists and environmental scientists at Lancaster University’s Environment Centre and Data Science Institute.

    Who should apply?

    We are looking for an enthusiastic student who is keen to explore the cutting-edge of data science in this important application area. Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in relevant subjects such as (but not limited to) Computer Science, Data Science, Statistics, Natural Sciences, Geography, or Environmental Sciences. We are particularly interested in applications from students who share our excitement about this potentially impactful research. The project will best suit a student with a strong quantitative background and experience or enthusiasm for gaining skills in modern programming techniques. Note that we do not expect to find a student with all the skill sets required to undertake this exciting, cross-disciplinary project and hence additional training will be provided once the student is in place.

    How to apply

    Please download the Data-smart ecosystem models information for the application process. 

  • Development of a Big Data-based drought forecasting system for the Mekong Delta Region

    Supervisors: Dr Xiaogang Shi (Lancaster University), Professor Andrew Binley (Lancaster University) and Professor Dien Pham Van (Vietnam National University of Forestry)

    Deadline for applications: Midnight 1 July

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

    Why is this project interesting?

    Droughts stand among the most damaging natural disasters in human, environmental and economic terms. In the Lower Mekong Basin, drought has been a prevalent concern for farmers over the last decades, especially for the Mekong Delta Region (MDR) that is the most productive region in agriculture and aquaculture in Viet Nam. Since late 2015, the MDR has experienced the longest and strongest drought period in nearly a century, severely affecting agricultural production of the region and the livelihood of local people. According to the Ministry of Agriculture and Rural Development (MARD) of Viet Nam, 1.5 million people in the MDR were in need of humanitarian assistance as of August 2016. The GDP growth in the first quarter of 2016 dropped to 5.6% while the first quarter growth for the same period in 2015 was 6.17% due to a serious reduction in exports of major goods produced in the MDR, including rice and seafood. Furthermore, the water level of the Mekong River has dropped to the lowest level since 1926. The low river levels have allowed seawater to penetrate 90 km inland, ruining vast swathes of cropland in the fertile delta and bringing considerable damage to the entire ecosystem. Therefore, improving drought prediction in the MDR is vital for stakeholders to make proper decisions on drought management and mitigation.

    Droughts can be monitored effectively using climatic drought indices. But there is limited capability to use these indices due to sparse observation networks. Now, with the advent of advanced remote sensing techniques, a range of Big Data sources have been recognized as useful tools for the large-scale area monitoring. We believe that the advances in land surface models, global Big Data sources, and data assimilation now make it possible to develop a regional drought monitoring and forecasting system, where drought predictions are most needed and in situ networks are sparse. This work will deliver a better understanding of the drought impacts for stakeholders, which would support increased preparedness and resilience to droughts and hence contribute to societal well-being, environmental sustainability, and economic growth in Viet Nam.

    What’s in it for you?

    Through the guidance of the supervisory team, you will develop an interdisciplinary way of approaching remote sensing and hydrologic hazards. Extensive training will be given to the student in the fundamentals of drought forecasting, climate informatics and data science. You will benefit from the research training programme offered at Lancaster University, by being part of the large and vibrant Lancaster Environment Centre and by becoming a member of the water and climate research group. Moreover, there is great potential for high quality academic publications of the results.

    Who should apply?

    We are seeking applications from graduates in a relevant subject area, such as hydrology, physical geography, and computing science. Graduates in mathematics, physics, and engineering with an interest in applying their skills to the environmental sciences are also welcome.

    How to apply

    Please download the Development of a Big Data-based drought forecasting system for the Mekong Delta information for the application process. 

  • Geophysical monitoring of the integrity of water-retaining earth structures

    Supervisors:  Prof. Andrew Binley (LU), Prof. Jon Chambers (BGS), Dr. Xiaogang (John) Shi (LU), Dr. Paul Wilkinson (BGS)

    Deadline for applications: Midnight 1 July

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

    Why is this project interesting?

    Failure of water-retaining earth structures (e.g. flood embankments and dams) can be devastating, in some cases leading to loss of life and/or huge engineering and environmental restoration. Current monitoring approaches are heavily dependent on either surface observations, which can only address failures that have already begun, or point sensors, which sample a very small volume of ground and are therefore inadequate as a means of detecting localized deterioration. This project aims to provide new approaches for monitoring the integrity of such geotechnical assets, by exploiting the potential of geophysical imaging approaches. Such approaches may enable volumetric tracking of structural changes associated with deterioration, motion of fluids, flow pathways and ground movement, thereby helping to prevent catastrophic failure by identifying problems at a much earlier stage. Although some attempts to use geophysics for this application have been made, recent developments in field-based geophysical experimentation and data processing need to be exploited in order to realise the full potential.  In this studentship we aim to bring together expertise at Lancaster and the British Geological Survey (BGS) in order to develop new approaches for monitoring the integrity of water-retaining earth structures.

    What’s in it for you?

    Solve real-world problems with industry partners. The project will involve very close engagement with industry partners, including working on pilot study sites provided by the Environment Agency and water companies, and collaborating with specialist geotechnical monitoring consultants and contractors. Become an expert in environmental and engineering geophysics. You will be given training in state-of-the-art geophysical techniques – both theory and practice.  We will also train you in numerical methods. Your training will help develop skills in quantitative geophysical methods, which can open up career opportunities in the environmental and exploration geophysics markets, where there is a clear demand, nationally and internationally, for numerate graduates. We recognise that a PhD profile should be international and we will ensure that opportunities exist for you to develop this (conference travel, publications, global networking).  Be part of a team. This project benefits from linkages with BGS, with whom you will gain additional training.  We have three geophysics PhD projects currently running between BGS and Lancaster University.  You will be part of this team and benefit from interactions with other projects. BGS will provide additional financial support (£1,000 per year). Develop international links. We will also provide an opportunity to engage with a large international network of environmental geophysicists. This will involve attending international meetings and conferences as well as opportunities for overseas fieldwork. Join an exciting research environment. You will join two internationally-recognised research groups. You will benefit from the research training programmes offered by the Faculty of Science and Technology at Lancaster University, and the additional training from the British Geological Survey, and by being part of the large and vibrant Lancaster Environment Centre.

    Who should apply?

    We are seeking applications from graduates or those who expect to graduate in 2018 with a good BSc or Masters degree. You should have a strong background in Earth Sciences, Geophysics, Physics or Engineering. You must have demonstrable potential for creative, high-quality PhD research.

    How to apply

    Please download the Geophysical monitoring of the integrity of water-retaining earth structures 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