Supervisors

Lead Supervisor

Dr Jozef Skakala, Plymouth Marine Laboratory, Modelling group
Additional Supervisors

Professor Prathyush P Menon, University of Exeter, Faculty of Environment, Science and Economy
Dr Juliane Wihsgott, Plymouth Marine Laboratory, Marine biogeochemistry group
David Ford, Met Office

Lead Institution: Plymouth Marine Laboratory

Location: Plymouth Marine Laboratory & University of Exeter

About the Partnership

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 eligible successful applicants, the studentships comprises:

• An stipend for 3.5 years (currently £18,622 p.a. for 2023-24) in line with UK Research and Innovation rates
• Payment of university tuition fee;
• 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

For information relating to the research project please contact the lead Supervisor via jos@pml.ac.uk  or https://www.pml.ac.uk/People/Dr-Jozef-Skakala 

Project Background
 

The ocean is an essential component of the climate system, whilst simultaneously providing key resources for billions of people. However, observing the ocean, especially its biology and chemistry, is a major challenge due to its vastness and complexity. Robust observations can be obtained from satellite measurements of the ocean surface, but only a limited number of ocean variables can be derived from those measurements, such as sea surface temperature, or surface chlorophyll concentrations. Other observations are scattered and still relatively few, as well as expensive to obtain. To make the best use of available resources, and achieve the goal of net-zero carbon science, we need to make our observations much more efficient, including being adaptive to real-time ocean conditions. Autonomous underwater vehicles (AUVs), such as ocean gliders, are establishing themselves as a key source of marine observations. Excitingly, these AUVs can be navigated by ``intelligent’’, digital twin (DT), systems, optimizing the effectiveness and maximising the impact of observational science by ensuring we target observations at the most interesting/important locations and times. These DT systems consist of two-way communicating physical-biogeochemical ocean models, machine learning (ML)/artificial intelligence (AI) components, and adaptive autonomous platforms (see Fig.1), with the ML/AI providing path-planning and sample control for the AUV. A successful example of such a system was demonstrated in a recent phytoplankton bloom-tracking study by Ford et al (2022), demonstrating that DTs including AUVs can (i) help us understand the onset of events potentially impacting marine water-quality, and (ii) improve forecasts of such events. The ideas from Ford et al (2022) need to be pushed further, e.g. by (i) extending them to new applications, (ii) exploring more effective DT designs, or (iii) including multiple AUVs. 

One important new application is focusing the AUVs on tracking the onset of marine hypoxia. This is of great value, since dissolved oxygen (DO) concentrations act as an important indicator of ecosystem health and water quality. The global DO inventory has declined by approximately 2% within the last 50 years and the depletion of DO is a known problem in parts of the North Sea. Hypoxia events can lead to mass fish mortality and, worryingly, are predicted to accelerate globally in response to climate change, expanding marine dead zones. Consequently, there are ongoing (e.g. OSPAR) oxygen monitoring programmes in the North Sea, to which future DT-AUV missions could contribute

Project Aims and Methods

We propose for the student to develop a proof-of-concept DT to demonstrate that multiple AUVs could be successfully navigated to investigate oxygen depletion/minima zones using their own measurements (of temperature, salinity, chlorophyll and oxygen itself) as well as Earth Observation (EO) and model data. The initial plan, which we are happy to later adapt to the student’s initiatives, needs and interests, is that the student will run virtual DT experiments using a state-of-the-art physics-biogeochemistry ocean model (NEMO-FABM-ERSEM), acting as the ``real-world ocean’’ in which AUVs take ``samples’’.  Virtual EO data will be similarly extracted from the model outputs and perturbed by noise to represent observational uncertainty, providing an idealised framework in which the true ocean state and all model and observational errors are known, allowing the effectiveness of the developed ML/AI algorithms to be accurately assessed. Building on a recent work of Skakala et al (2023), the student will develop ML model to forecast DO values from the virtual glider measurements and EO data. The ML model will then be used together with path-planning algorithms, designed by the student, to optimize the trajectory and sampling strategy of multiple gliders. The work will demonstrate the feasibility of the DT AUVs control for tracking hypoxia and provide framework for real-world applications. The work will further investigate/advise what variables need to be measured, as well as their locations and sampling resolution, for a successful future hypoxia tracking mission.

Candidate requirements

1. high level numerical/technical skills, e.g. background in one of the following: computer science, mathematics, physics, engineering., 2. enthusiasm for marine/climate science.

Project partners

This is a highly successful partnership between PML, University of Exeter and the Met Office (here as a collaborative partner), resulting in the first ever example of a fully autonomous marine biogeochemistry observing mission, published in Ford et al (2022). PML provides a vibrant environment for all aspects of world-class marine science, from observations and autonomy (e.g. DIMA group, EOSA/NEODAAS, or WCO/SmartSound Plymouth) to modelling and data assimilation (MSM group). University of Exeter is a world-leading institution in environmental and climate research, as well as in applied AI/ML, including applying AI/ML solutions to AUV path-planning and navigation. Met Office is a world-leader in weather and climate prediction (including the ocean). The student will be based at PML, but will spend a major part of their PhD at the University of Exeter, as well as visiting the Met Office, benefiting from the expertise of scientists at the UK’s national meteorological service.

Training

PML will provide many opportunities to interact and be mentored by leading experts in many aspects of marine science (especially in biogeochemistry/ecology) and together with the Met Office will provide the student with access and introduction to the state-of-the-art marine models and HPC. Uni of Exeter will offer many useful courses, e.g. on robotics and automation, statistical modelling in space and time, high performance computing as well as general training programmes, e.g. on research methods.

Background reading and references

1. Ford, D.A., Grossberg, S., Rinaldi, G., Menon, P.P., Palmer, M.R., Skákala, J., Smyth, T., Williams, C.A., Lorenzo Lopez, A. and Ciavatta, S., 2022. A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts. Frontiers in Marine Science, 9, p.1067174. 2. Skakala, J., Awty-Carroll, K., Menon, P.P., Wang, K. and Lessin, G., 2023. Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia. Frontiers in Marine Science, 10, p.1058837.

Eligibility

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 2023 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.

Equality, Diversity and Inclusion

The University of Exeter is committed to promoting and supporting equality, diversity, and inclusion within our working environments and is at the heart of all our activities.  With over 27,000 students and 6,400 staff from 180 different countries we offer a diverse and engaging environment where our diversity is celebrated and valued as a major strength. 
We actively encourage applicants with varied experiences and backgrounds and from all sections of the community regardless of age, race, ethnicity, sexual orientation, gender, religion, or disability status.  We are committed to creating an inclusive culture where all members of our community are supported to thrive.
Whilst all applicants will be judged on merit alone, we particularly welcome applications from groups currently underrepresented within our postgraduate research student community.  Reasonable adjustments are available for interviews and workspaces.

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 http://www.exeter.ac.uk/postgraduate/apply/english/.

How to apply

Apply now

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
• GW4+ DTP Personal Statement - https://tinyurl.com/yhzvmduc  Please upload your completed Personal Statement  using the link to the form.  You must use this Personal Statement form as part of the application process.  No other format for cover letter or personal statement will be accepted.
• 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 pgrapplicants@exeter.ac.uk, 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 2359 hours GMT Tuesday 9 January 2024. Interviews will be held between 22 February and 8 March 2024.  For more information about the NERC GW4+ DPT please visit https://nercgw4plus.ac.uk

If you have any general enquiries about the application process please email pgrapplicants@exeter.ac.uk  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.

Summary