Supervision Style
Read on to see if you think I would be a compatible supervisor for you. If so, projects being currently advertised can be found below!
How a PhD would work
Each student has a dedicated 1h meeting each week. Any contact outside of that will be on-demand and will vary with the project, for example we may meet more when students are beginning with a new programming language or when results come in. Second supervisors will meet with us at least once a semester or up to every week, depending on how involved they are with the research topic. I encourage students to attend departmental events such as journal clubs and international seminars to gain an awareness of the wider field and build a research culture with our colleagues. I help students select training courses, conferences and summer schools to suit the research requirements and the career goals of each student.
Supervision Philosophy
Mutually beneficial research. A PhD student is not a “research minion”. In suggesting a project relating to my research themes, I am hoping that you will push that research further and explore the question. I will also usually suggest a starter project. However, the later research directions are chosen with respect to both our interests; for example, to make sure that a student trains in the skills related to their career aims or to achieve knowledge of a given field.
Student independence. All my student projects are structured to develop the student’s independence. All projects will start with significantly more supervision, which should naturally taper off. For example, by the end of an undergraduate project, a student should be understand the subject area and start to suggest new questions and identify new areas to learn. This is part of teh undergraduate journey to becoming an independent learner. By the end of PhD projects, students should be able to not only talk confidently about their subject and suggest new questions which will increase the knowledge in our field, but also suggest projects to explore and answer those questions.
Individual learning styles. I am a strong believer in adapting my supervision styles to each student, providing different amounts of support in different areas. For example, some students will need more help to learn skills such as project management (e.g. keeping a project on time, setting and completing achievable goals) while others might need help on presentation skills. One of the skills we expect students to learn throughout a PhD is how to work effectively with increasingly less external guidance. We would start a PhD by having a discussion of supervision styles and what our expectations of each other are.
Providing a welcoming research environment. Much of science today is completed by collaboration, and certainly scientists with effective networks have more opportunities. For this, a culture of regularly critically discussing our own work, weekly research seminars and journal clubs, and a welcoming group (e.g. our large and social PhD cohort) are very important. We regularly (at least once a term) discuss EDI and the barriers faced by communities who are underrepresented in academia – with the goal of becoming effective allies and working out what we can do to make our community more accessible and more welcoming.
I also aim to regularly revisit my supervision style, so that both I and any PhD student continue to get what we want out of a project.
Currently/recently offered projects
Unique PhD Opportunity for Students in/from North-West England
This year we have a unique set of funding solely to support a student from North-West England to complete a maths/physics PhD. Some potential topics I would be willing to supervise include:
- Radiation belt modelling (including the numerical methods behind modelling via PDEs or the space weather effects)
- ULF waves generation, propagation and impact on the radiation belts
- Complex nonlinear systems methods in space
- Machine learning /AI in space (I have experience with a variety of techniques; we would need to match technique and application)
- Group theory and how the breaking of symmetries leads to particle motions in near-Earth space
- How the theory behind graph neural networks applies to spatiotemporal applications, e.g. forecasting
Please contact me if you are interested in a PhD in maths or physics and may be eligible; we can discuss potential projects. If we do not have interests in common I can direct you to other potential supervisors.
This project is offered via NUdata, an STFC funded collaboration between Newcastle and Northumbria universities which provides additional machine learning /AI courses and industry placements. The project itself has a clearly defined starter project. However, as part of the project goals will be to identify (or develop) methods we can use in space weather data analysis there is a lot of freedom in this project going forward.
Using AI to find the giant magnetospheric waves driving near-Earth space
This project is offered with STFC funding. The goal is to use a machine learning method to classify ULF (ultra-low frequency) waves and then use these results to further our understanding of the system (i.e. the space physics and/or space weather applications, including Earth’s radiation belts)
Non-specified project opportunities
Both the NUdata and STFC adverts above can also include projects that are not already specified. I would be happy to work with you to outline potential PhD projects; send me an email with your CV and proposed project areas. If I think any related project submissions may be successful, I would suggest a meeting to discuss potential projects. After our meeting, you would need to write a research proposal expanding on this discussion. The NUdata opportunity must involve data science/AI in a field of STFC science, while the second opportunity must only be on STFC-related science (e.g. I can supervise space phsyics, space weather forecasting)
Candidate selection
Please note that our projects are very competitive. In general, we rank applicants using criteria such as motivation, project-specific skills, their approaches to independent study and their previous qualification. You are welcome to contact me or the contacts mentioned in our adverts for more information; if I can’t answer myself I will pass you on to the person I think can best help.
Current supervision
Characterising Earth’s Magnetic Field with Graph Neural Networks Start Date: 01/10/2022