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Award details

Data Enabled Adaptive AUV Sampling & Statistical Modelling for Marine Ecosystem Monitoring & Prediction, NERC GW4+ DTP PhD studentship for 2022 Entry, PhD in Engineering Ref: 4242

About the award


Lead Supervisor

Prathyush P Menon, College of Engineering, Mathematics and Physical Science, University of Exeter

Additional Supervisors

Jozef Skakala, Plymouth Marine Laboratory, MSM

Stefano Ciavetta, Plymouth Marine Laboratory, MSM

Location: Streathum Campus, University of Exeter, Exeter, Devon

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology,  the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see

For eligible successful applicants, the studentships comprises:

  • An stipend for 3.5 years (currently £15,609 p.a. for 2021/22) in line with UK Research and Innovation rates
  • Payment of university tuition fees;
  • A research budget of £11,000 for an international conference, lab, field and research expenses;
  • A training budget of £3,250 for specialist training courses and expenses

Project details

Project Background

Managing UK seas to maintain clean, healthy, safe, productive and biologically diverse, requires comprehensive information about the state of our seas. Accurate prediction/forecast of spatiotemporal phenomena such as spring blooms, harmful algal blooms from environmental parameters such as temperature, salinity, chlorophyll, upwelling, and rainfall indices allows to better understand ecosystem variability. This requires combination of numerical models and observational techniques. There exist multiple methods including fixed observation points (for e.g. L4 station), ship based oceanography and remote sensing using satellites to aid. Along with all these still being used, the use of Autonomous Underwater Vehicles (AUVs) for oceanographic sampling of such environmental parameters has multiple benefits: (i) accessibility to a wider 3 dimensional region at a greater scale (at required depth), (ii) gathering of high quality in-situ dynamic information/data and (iii) greater endurance. A key aspect in the use of AUVs depends on the locations at which it needs to take measurements. Ensuring the traverse of the AUVs for gathering data along the most probable regions and how the gathered information assimilated to the existing short term forecast models to reduce uncertainty in prediction are key to a successful programme. The proposed project aims to address some of the challenges in this line of research. Beyond the prediction/monitoring of presence of chlorophyll indicating the algal blooms, the concept is expandable to other environmental problems such as deoxygenation, acidification and eutrophication. Plymouth Marine Laboratory and University of Exeter has been collaborating in this area of research since 2018.

Project Aims and Methods

The envisaged main components of the research are: a short term forecast model, an iterative algorithm to plan the paths for the AUVs, data based validation plans. The evolution of the features over the 3 dimensional space follows a complex spatiotemporal dynamics. This dynamics and internal principles associated with the phenomenon needs to be learned with in the short term forecast model. Machine learning based methods, which use available historical data, have often found to be helpful for making predictions with associated level of confidence bound. Gaussian process based method is an interesting candidate. The research candidate will carry out an initial literature review on different useful available methods for spatiotemporal sequence forecasting, assess the advantages and disadvantages of the methods from the perspective of current problem listed above. Based on the trade off on computational complexity and accuracy, the research student will develop advanced methods and codes for generating appropriate short term spatiotemporal sequence forecast model of the spring blooms over a region (regular and irregular grids cab be considered). The research student will receive support on machine learning and numerical model development from University of Exeter. The research student will have access to all the relevant historical data set, and continuous support based on the domain expertise on the physical-biogeochemical spatiotemporal features from the PML team. The student will have the opportunity to test the tools/codes for generating the spatiotemporal sequence forecast model with other sets of environmental parameters such as oxygen, pH, etc. Based on the outcome of the mathematical model, an iterative algorithm will be developed by the research student to find a trajectory for the AUVs that maximises an information quality metric for e.g. this could be variance reduction, information gain or the mutual information. The research student will have the opportunity to work at Unmanned Systems Control & Autonomy Lab at CFCM. The research student can benefit from the opportunity the lab facility provides to test some of the algorithms in an indoor emulated environments using existing available unmanned robotic systems, and the realistic testing depends entirely on the preference of the research student (as it is not a primary goal of the project as stated above). Further different constraints such as fuel, energy, time, no-go zones, and bathymetry constraints will also be considered in a step by step manner. A balance of exploration and exploitation of the search space will also be considered. The data gathered while traversing through the proposed trajectory will be made use to assimilate with the forecast model to enhance the confidence in predictions (or reduce the levels of uncertainty). A data based validation of the proposed probabilistic short term forecast model with the iterative algorithms to plan the paths for AUVs will be carried out and demonstrated to wider community.

Candidate requirements

Indicate if specific skills or disciplines are required. Mechanical/Electrical/Mathematics/Computer Science Coding of MATLAB/Simulink necessary (Python expertise also useful) Interest in AUVs, Oceanography, Machine Learning

Project partners

Highlight the exciting research collaborations, resources and student opportunities provided by the GW4+ Research Organisations, GW4 Universities, CASE partners and Collaborative Partners on the project


Describe any specialist training, fieldwork and overseas opportunities.

Background reading and references

Font size may be reduced to 10pt to allow for additional space elsewhere and to keep the advertisement to two pages Skákala, Jozef, et al. "The assimilation of phytoplankton functional types for operational forecasting in the northwest European shelf." Journal of Geophysical Research: Oceans 123.8 (2018): 5230-5247. Skákala, Jozef, et al. "Towards a multi‐platform assimilative system for North Sea biogeochemistry." Journal of Geophysical Research: Oceans 126.4 (2021): e2020JC016649 Owen, N.E. Challenor, P, Menon, P.P., Bennani S. “Comparison of Surrogate-Based Uncertainty Quantification Methods for Computationally Expensive Simulators”, SIAM Journal of uncertainty quantification, 2017. Mellucci, C., Menon, P. P., Edwards, C., & Challenor, P. (2016, December). Predictive oceanic features tracking with formations of autonomous vehicles. In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE. C. Mellucci, P. P. Menon, C. Edwards and P. G. Challenor, "Environmental Feature Exploration With a Single Autonomous Vehicle," in IEEE Transactions on Control Systems Technology, vol. 28, no. 4, pp. 1349-1362, July 2020. Rinaldi, G., Menon, P. P., and Edwards C. "Suboptimal Sliding Mode-based Heading and Speed Guidance Scheme for Boundary Tracking with Autonomous Vehicle." 2021 American Control Conference (ACC). IEEE, 2021

Useful links For information relating to the research project please contact the lead Supervisor via


NERC GW4+ DTP studentships are open to UK and Irish nationals who, if successful in their applications, will receive a full studentship including payment of university tuition fees at the home fees rate.

A limited number of full studentships are also available to international students which are defined as EU (excluding Irish nationals), EEA, Swiss and all other non-UK nationals.  For further details please see the NERC GW4+ website.

Those not meeting the nationality and residency requirements to be treated as a ‘home’ student may apply for a limited number of full studentships for international students. Although international students are usually charged a higher tuition fee rate than ‘home’ students, those international students offered a NERC GW4+ Doctoral Training Partnership full studentship starting in 2022 will only be charged the ‘home’ tuition fee rate (which will be covered by the studentship). 

International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD. More information on this is available from the universities you are applying to (contact details are provided in the project description that you are interested in.

The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.



Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the  project


How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

  • CV
  • Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.
  • Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.
  • If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English, please see the entry requirements for details.
  • Two references

Reference information
You will be asked to submit two references as part of the application process.  If you are not able to upload  your reference documents with your application please ensure you provide details of your referees.  If you provide contact details of referees only, we will not expect receipt of references until after the shortlisting stage. Your referees should not be from the prospective supervisory team.

If you are shortlisted for interview, please ensure that your two academic referees email their references to the, 7 days prior to the interview dates. Please note that we will not be contacting referees to request references, you must arrange for them to be submitted to us by the deadline.

References should be submitted by your referees to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is 1600 hours GMT Friday 10 January 2022. Interviews will be held between 28 February and 4 March 2022.  For more information about the NERC GW4+ DPT please visit

If you have any general enquiries about the application process please email  Project-specific queries should be directed to the lead supervisor.

Data Sharing
During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

  • the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
  • administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.


Application deadline:10th January 2022
Value:£15,609 per annum for 2021-2022
Duration of award:per year
Contact: PGR Enquiries