Research Fellow in Data-driven Dynamical Models of Earth's Core
| Dyddiad hysbysebu: | 11 Mehefin 2025 |
|---|---|
| Cyflog: | £39,355 i £46,735 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 11 Gorffennaf 2025 |
| Lleoliad: | Leeds, West Yorkshire |
| Gweithio o bell: | Ar y safle yn unig |
| Cwmni: | University of Leeds |
| Math o swydd: | Dros dro |
| Cyfeirnod swydd: | ENVEE1816 |
Crynodeb
This role will be based on the university campus, with scope for it to be undertaken in a hybrid manner. We are also open to discussing flexible working arrangements.
Are you an ambitious researcher looking for your next challenge? Do you have a background in machine learning or fluid dynamics and an interest in applying your skills to understand the dynamics of Earth’s fluid core and space-weather hazard? Do you want to further your career in one of the UK’s leading research intensive Universities?
We are seeking a Research Fellow to fulfil a key role in our project to 1) produce new data-driven models of the magnetohydrodynamics of Earth’s core and 2) better understand and predict the south Atlantic Anomaly (SAA), a region of weak magnetic field intensity in the south Atlantic in which spacecraft are exposed to high energy radiation. The SAA is the primary focus of the recent Macau satellite mission MSS-1 whose observations will be integral to the project. You will be based in the deep Earth research group within the School of Earth and Environment at the University of Leeds and work closely with Professors Phil Livermore and Chris Davies (SEE), collaborating with Drs William Brown and Ciaran Beggan at the British Geological Survey (BGS). You will also work with a variety of external project partners: Macau Institute of Space Technology and Application, British Antarctic Survey, and the Technical University of Denmark; and a project stakeholder group including the Met office, the European Space Agency, RiskAware and RAL Space. There will be travel opportunities to work in-person with this network of collaborators, as well as for presenting the research at national and international conferences. This work is part of the NERC-funded project “A new paradigm for the geodynamo: data-driven models of core dynamics that explain and predict Earth’s magnetic shield” between the University of Leeds and BGS.
You will begin by mapping the SAA using both geomagnetic and high-energy particle data as measured by the MSS-1 mission. You will also (if required) learn how to use physics-informed neural networks (PINNs) and how they can be applied to the fluid dynamics of Earth’s core where the global magnetic field is generated. Using a numerical geodynamo simulation as a benchmark, you will undertake a thorough investigation of both local and global PINN reconstructions of the modelled core using sparse synthetic magnetic field observations. Next, you will develop a suite of PINN models, simultaneously constrained by the equations of magnetohydrodynamics and global magnetic satellite data from 1999-present including data from MSS-1. You will use these models to infer dynamics and structures hidden from observation, such as the internal flow, magnetic field, temperature, and stratification profile. Finally, you will use these models to investigate the underlying dynamics of the SAA, and predict its future and associated space-weather hazard over the next 20 years. Throughout the project, you will help coordinate 6-monthly meetings with the project partners and stakeholders, ensuring two-way communication about project findings but also areas to focus on to maximise impact.
You will have a PhD (or close to completion) in geophysics, physics, applied mathematics or similar highly numerical discipline with a strong background in computational modelling or scientific machine learning. You will also have the ability to conduct independent research and a developing track record of publications in international journals. In addition, you will have excellent communication, planning, and team working skills.
Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information please visit: www.gov.uk/skilled-worker-visa.
For research and academic posts, we will consider eligibility under the Global Talent visa. For more information please visit: https://www.gov.uk/global-talent.
What we offer in return
26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) – that’s 42 days a year!
Generous pension scheme plus life assurance – the University contributes 14.5% of salary.
Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
Access to on-site childcare, shopping discounts and travel schemes are also available.
And much more!
Are you an ambitious researcher looking for your next challenge? Do you have a background in machine learning or fluid dynamics and an interest in applying your skills to understand the dynamics of Earth’s fluid core and space-weather hazard? Do you want to further your career in one of the UK’s leading research intensive Universities?
We are seeking a Research Fellow to fulfil a key role in our project to 1) produce new data-driven models of the magnetohydrodynamics of Earth’s core and 2) better understand and predict the south Atlantic Anomaly (SAA), a region of weak magnetic field intensity in the south Atlantic in which spacecraft are exposed to high energy radiation. The SAA is the primary focus of the recent Macau satellite mission MSS-1 whose observations will be integral to the project. You will be based in the deep Earth research group within the School of Earth and Environment at the University of Leeds and work closely with Professors Phil Livermore and Chris Davies (SEE), collaborating with Drs William Brown and Ciaran Beggan at the British Geological Survey (BGS). You will also work with a variety of external project partners: Macau Institute of Space Technology and Application, British Antarctic Survey, and the Technical University of Denmark; and a project stakeholder group including the Met office, the European Space Agency, RiskAware and RAL Space. There will be travel opportunities to work in-person with this network of collaborators, as well as for presenting the research at national and international conferences. This work is part of the NERC-funded project “A new paradigm for the geodynamo: data-driven models of core dynamics that explain and predict Earth’s magnetic shield” between the University of Leeds and BGS.
You will begin by mapping the SAA using both geomagnetic and high-energy particle data as measured by the MSS-1 mission. You will also (if required) learn how to use physics-informed neural networks (PINNs) and how they can be applied to the fluid dynamics of Earth’s core where the global magnetic field is generated. Using a numerical geodynamo simulation as a benchmark, you will undertake a thorough investigation of both local and global PINN reconstructions of the modelled core using sparse synthetic magnetic field observations. Next, you will develop a suite of PINN models, simultaneously constrained by the equations of magnetohydrodynamics and global magnetic satellite data from 1999-present including data from MSS-1. You will use these models to infer dynamics and structures hidden from observation, such as the internal flow, magnetic field, temperature, and stratification profile. Finally, you will use these models to investigate the underlying dynamics of the SAA, and predict its future and associated space-weather hazard over the next 20 years. Throughout the project, you will help coordinate 6-monthly meetings with the project partners and stakeholders, ensuring two-way communication about project findings but also areas to focus on to maximise impact.
You will have a PhD (or close to completion) in geophysics, physics, applied mathematics or similar highly numerical discipline with a strong background in computational modelling or scientific machine learning. You will also have the ability to conduct independent research and a developing track record of publications in international journals. In addition, you will have excellent communication, planning, and team working skills.
Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information please visit: www.gov.uk/skilled-worker-visa.
For research and academic posts, we will consider eligibility under the Global Talent visa. For more information please visit: https://www.gov.uk/global-talent.
What we offer in return
26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) – that’s 42 days a year!
Generous pension scheme plus life assurance – the University contributes 14.5% of salary.
Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
Access to on-site childcare, shopping discounts and travel schemes are also available.
And much more!