Supervisors

• Dr Philip Donkersley (Lancaster Environment Centre)
• Jenny Roberts (Engineering)
• Dr Alex Bush (Lancaster Environment Centre)
• Dr Tim Landgraf (Berlin)

Dates

• Deadline for applications: 15th March 2024
• Provisional Interview Date: Mar - April 2024
• Start Date: October 2024

Project description

Bees are facing an ever-growing series of threats, from land use changes to new diseases to chemical pesticides. The overwhelming majority of our knowledge of how these threats impact bees comes from studying bees out in the wild when they are pollinating. Bees typically spend 90% of their life inside the nest, but very little research has studied how behaviour here is impacted by things like neonics. This project will develop an identifying and decoding algorithm using an infrared camera based on an algorithm designed for honeybees to automate the study of bumblebee behaviour inside the nest. The algorithm will be trained to recognise cleaning, feeding, building and foraging behaviours and generate data sets to show how bees chose to spend their nest time can be disrupted by external forces.

General eligibility criteria

 Applicants would normally be expected to hold a minimum of a UK Honours degree at 2:1 level or equivalent in a relevant degree course.

Project-specific criteria

The ideal candidate will have a firm grasp of computer science and programming language, and be comfortable handling live insects, in particular honeybees and bumblebees.

Studentship funding

A tax-free stipend will be paid at the standard UKRI rate; currently £18,622. This is a fully funded studentship of 3.5 years for UK/Home students.

Enquiries

Interested applicants are welcome to get in touch to learn more about the PhD project. Please contact Philip Donkersley (p.w.donkersley1@lancaster.ac.uk) for more information.

Further reading

• Crall, J.D., Switzer, C.M., Oppenheimer, R.L., Ford Versypt, A.N., Dey, B., Brown, A., Eyster, M., Guérin, C., Pierce, N.E., Combes, S.A. and de Bivort, B.L., (2018) Neonicotinoid exposure disrupts bumblebee nest behaviour, social networks, and thermoregulation. Science, 362, 683-686.
• Dormagen, D.M., Wild, B., Wario, F. and Landgraf, T., (2023) Machine learning reveals the waggle drift's role in the honey bee dance communication system. PNAS Nexus, 2, 275-275.
• Gernat, T., Jagla, T., Jones, B.M., Middendorf, M. and Robinson, G.E., (2023) Automated monitoring of honey bees with barcodes and artificial intelligence reveals two distinct social networks from a single affiliative behaviour. Scientific reports, 13, 1541-1541.

Application process

1. Download the Natural Sciences Funded PhD Application Form and Natural Sciences Funded PhD Reference Form‌
2. Complete the Application Form, renaming the document with your 'Name and Application Form' e.g., Joe Bloggs Application Form.
3. Submit the completed Application Form and a CV to naturalsci@lancaster.ac.uk
4. Please note only Word or PDF files are accepted.
5. 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 naturalsci@lancaster.ac.uk
6. Please note only Word or PDF files are accepted. It is important that you ensure references are submitted by the closing date or as soon as possible.
7. You will receive a generic acknowledgement in receipt of successfully sending the application documents.
8. Please note that only applications submitted as per these instructions will be considered.
9. Please note that 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.
10. Please note that 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.

Supervisors

• Israel Martinez Hernandez (Mathematics and Statistics)
• Emma Eastoe (Mathematics and Statistics)
• Suzana Ilic (Lancaster Environment Centre)
• Rob Lamb (JBA Trust)

Dates

• Deadline for applications: 15th March 2024
• Provisional Interview Date: Mar - April 2024
• Start Date: October 2024

Project description

Climate change has led to an increased risk for many natural, such as flooding, storms, coastal erosion, and drought, resulting in significant challenges for communities and potential damage to the economy. To prevent or mitigate damage from such events, it is crucial to accurately predict the behaviour of future events. 

This PhD project develops a statistical modelling framework for flood events using a functional time series approach. Unlike existing methods, which model only the peak and magnitude of events, the proposed approach will provide new scientific insights into how floods evolve over time and space and pave the way for new operational tools for risk prediction, particularly for multi-hazard events. 

This PhD project is cross-disciplinary with LEC and JBA. Thus, the student will have the opportunity to work with JBA to apply the methodology to compound hazards problems.

General eligibility criteria

Applicants would normally be expected to hold a minimum of a UK Honours degree at 2:1 level or equivalent in a relevant undergraduate degree course.

Project-specific criteria

The ideal candidate will have a master’s level understanding of statistical modelling, including generalised linear models and either time series analysis or geostatistics. You should be confident with at least one of likelihood and Bayesian inference and undergraduate-level multivariate probability. We encourage students who do not have this experience to make informal enquiries prior to making an application.  

Studentship funding

A tax-free stipend will be paid at the standard UKRI rate; currently £18,622. This is a fully funded studentship of 3.5 years for UK/Home students.

Enquiries

Interested applicants are welcome to get in touch to learn more about the PhD project. Please contact Israel Martinez Hernandez (i.MartinezHernandez@lancaster.ac.uk) or Emma Eastoe (e.eastoe@lancaster.ac.uk) for more information. 

Further reading

• Eastoe, E. F. (2019). Non-stationarity in peaks-over-threshold river flows: a regional random effects model. Environmetrics, 30(5), Article e2560. https://doi.org/10.1002/env.2560 
• Martínez-Hernández, I. and Genton, M. (2023), Surface time series models for large spatio-temporal datasets. Spatial Statistics, (53) 1001718. https://doi.org/10.1016/j.spasta.2022.100718 
• Quintela-del-Río, A. and Francisco-Fernández, M. (2018), River flow modelling using nonparametric functional data analysis. J Flood Risk Management, 11: S902-S915. https://doi.org/10.1111/jfr3.12282 

Application process

1. Download the Natural Sciences Funded PhD Application Form and Natural Sciences Funded PhD Reference Form‌.
2. Complete the Application Form, renaming the document with your 'Name and Application Form' e.g., Joe Bloggs Application Form.
3. Submit the completed Application Form and a CV to naturalsci@lancaster.ac.uk
4. Please note only Word or PDF files are accepted.
5. 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 naturalsci@lancaster.ac.uk
6. Please note only Word or PDF files are accepted. It is important that you ensure references are submitted by the closing date or as soon as possible.
7. You will receive a generic acknowledgement in receipt of successfully sending the application documents.
8. Please note that only applications submitted as per these instructions will be considered.
9. Please note that 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.
10. Please note that 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.

Supervisors

• Samuel Taylor (Lancaster Environment Centre)
• James Taylor (Engineering)
• David Cheneler (Engineering)

Dates

• Deadline for applications: 15th March 2024
• Provisional Interview Date: Mar - April 2024
• Start Date: October 2024

Project description

AI and computer vision have been underpinning a revolution in the throughput and scale of measurements of plant-environment interactions, helping to accelerate global action against hunger by improving food crop adaptation to rapidly changing climate and severe weather events.

To design routes to genetic improvement, approaches that can image whole plants or groups of plants need to be linked with underpinning physiology and determined at the finer spatial scales that determine the physiological responses of individual plants.

This project will use state-of-the-art facilities at Lancaster University to develop and evaluate data-based mechanistic modelling approaches and sensing platforms for measuring plant physiology and ground truthing against data from a new, high-throughput phenotyping centre.

With the goal of bringing next-generation, AI-supervised agricultural data pipelines to relevant field environments, you will develop your research project with an interdisciplinary team of supervisors and be among the first to access our new facilities.

General eligibility criteria

Applicants would normally be expected to hold a minimum of a UK Honours degree at 2:1 level or equivalent in a relevant degree course.

Project-specific criteria

The candidate should have an interest in agricultural and biological systems, and will be numerate, ideally with some engineering or computer science experience.

Studentship funding

A tax-free stipend will be paid at the standard UKRI rate; currently £18,622. This is a fully funded studentship of 3.5 years for UK/Home students.

Enquiries

Interested applicants are welcome to get in touch to learn more about the PhD project. Please contact Dr Samuel Taylor, s.taylor19@lancaster.ac.uk, for more information.

Further reading

• Furbank RT, Jimenez-Berni JA, George-Jaeggli B, Potgieter AB, Deery DM (2019) Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytologist 223: 1714-1727. https://doi.org/10.1111/nph.15817
• Beadle J, Taylor CJ, Ashworth K, Cheneler D (2020) Plant leaf position estimation with computer vision. Sensors 20, 5933: 1–16. https://doi.org/10.3390/s20205933
• Burrell T, Fozard S, Holroyd GH, French AP, Pound MP, Bigley CJ, Taylor CJ, Forde BG (2017) The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology. Plant Methods 13: 1–20. https://doi.org/10.1186/s13007-017-0158-6
• Tsitsimpelis I, Wolfenden I, Taylor CJ (2016) Development of a grow–cell test facility for research into sustainable controlled-environment agriculture. Biosystems Engineering 150: 40–53. https://doi.org/10.1016/j.biosystemseng.2016.07.008

Application process

1. Download the Natural Sciences Funded PhD Application Form and Natural Sciences Funded PhD Reference Form‌
2. Complete the Application Form, renaming the document with your 'Name and Application Form' e.g., Joe Bloggs Application Form.
3. Submit the completed Application Form and a CV to naturalsci@lancaster.ac.uk
4. Please note only Word or PDF files are accepted.
5. 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 naturalsci@lancaster.ac.uk
6. Please note only Word or PDF files are accepted. It is important that you ensure references are submitted by the closing date or as soon as possible.
7. You will receive a generic acknowledgement in receipt of successfully sending the application documents.
8. Please note that only applications submitted as per these instructions will be considered.
9. Please note that 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.
10. Please note that 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.

Supervisors

• Jess Davies(Lancaster Environment Centre)
• Andy Marshall (Physics)
• Jenny Roberts (Engineering)
• John Quinton (Lancaster Environment Centre)

Dates

• Deadline for applications: 15th March 2024
• Provisional Interview Date: Mar - April 2024
• Start Date: October 2024

Project description

In this discipline-spanning PhD, you will help revolutionize our ability to sense soil greenhouse gas (GHG) emissions, in turn, helping to mitigate climate change, support sustainable farming and land management, and open up new frontiers for environmental research.

Soils are key to climate change: contributing to 25% of global GHG emissions from agriculture and land use. They are also a substantial potential carbon sink. However, the high costs and power requirements of current in-situ sensing solutions hinder our ability to monitor soil GHG emissions, prohibiting effective policy-making, land management and the creation of soil carbon markets.

Working with a supervisory team spanning soil sciences, engineering, and physics, you will combine exciting developments in quantum sensing with signal processing and lean manufacturing to help address this challenge and deliver cheaper, more accessible soil GHG sensing solutions that help farmers, policymakers and scientists in securing a sustainable future.

General eligibility criteria

Applicants would normally be expected to hold a minimum of a UK Honours degree at 2:1 level or equivalent in a relevant degree course.

Project-specific criteria

The ideal candidate will have an interest in sensing technologies, a strong desire to contribute to sustainable solutions and prior knowledge of or curiosity for learning more about soil ecosystems. Quantitative skills that can contribute to signal processing work and experience in fabrication and laboratory testing and development are highly desirable to help support success in this PhD.

Studentship funding

A tax-free stipend will be paid at the standard UKRI rate; currently £18,622. This is a fully funded studentship of 3.5 years for UK/Home students.

Enquiries

Interested applicants are welcome to contact Jess Davies at jess.davies@lancaster.ac.uk to learn more about the PhD project.

Further reading

• Smith, P., Soussana, J.F., Angers, D., Schipper, L., Chenu, C., Rasse, D.P., Batjes, N.H., Van Egmond, F., McNeill, S., Kuhnert, M. and Arias‐Navarro, C., 2020. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Global Change Biology, 26(1), pp.219-241.
• Oertel, C., Matschullat, J., Zurba, K., Zimmermann, F. and Erasmi, S., 2016. Greenhouse gas emissions from soils—A review. Geochemistry, 76(3), pp.327-352.
• Davies, J. The business case for soil. Nature 543, 309–311 (2017). https://doi.org/10.1038/543309a

Application process

1. Download the Natural Sciences Funded PhD Application Form and Natural Sciences Funded PhD Reference Form‌.
2. Complete the Application Form, renaming the document with your 'Name and Application Form' e.g., Joe Bloggs Application Form.
3. Submit the completed Application Form and a CV to naturalsci@lancaster.ac.uk
4. Please note only Word or PDF files are accepted.
5. 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 naturalsci@lancaster.ac.uk
6. Please note only Word or PDF files are accepted. It is important that you ensure references are submitted by the closing date or as soon as possible.
7. You will receive a generic acknowledgement in receipt of successfully sending the application documents.
8. Please note that only applications submitted as per these instructions will be considered.
9. Please note that 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.
10. Please note that 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.

There are no funded projects for the Envision Doctoral Training Partnership at this time.