Fully-funded PhD positions and post-docs
Applicants for a PhD should have a strong academic track record (a first-class, or equivalent) in a scientific, mathematical, or engineering discipline. Background in computational physics / mathematics and scientific computing are an advantage. Applicants for a post-doc should have a PhD in a scientific, mathematical, or engineering discipline with a publication record. Some experience of scientific computing is essential.
Current funding (ERC Starting Grant): Physics-constrained adaptive learning for multi-physics optimization
Current funding (UKRI ExCalibur): Turbulence at the exascale: application to wind energy, green aviation, air quality and net-zero combustion
Exascale Computing Grants
27/8/2022: One postdoctoral position is available in Quantum algorithms for the simulation of turbulent flows. Please, apply through this link https://www.imperial.ac.uk/jobs/description/ENG02271/research-assistant-associate-quantum-algorithms-simulation-turbulent-flows/, if you are interested. Deadline 31st October 2022.
Past fully funded positions
1/6/2022. One fully-funded PhD studentship in Physics-aware machine learning for multi-physics flows.
04/2022. Fully-funded post-doc position in "Physics-aware machine learning for exascale fluid mechanics" funded by UKRI/EPSRC
01/2022. Fully funded post-doc in "Research Associate in machine learning for multi-phase flows", funding from EPSRC, Programme Grant PREMIERE
09/2021. Fully funded PhD studentship "Research Assistant in Physics-aware machine learning", funding from (ERC) starting grant, PhyCo project.
09/2021. Fully funded PhD studentship "Research Assistant in Physics-aware data assimilation", funding from (ERC) starting grant, PhyCo project.
09/2021. Fully funded post-doc position in Physics-aware of machine learning for flow optimization, funding from (ERC) starting grant, PhyCo project.
09/2021. Fully funded PhD studentship “Physics-aware machine learning for complex flows”, funding from EPSRC
09/2021. Fully funded PhD studentship “Physics-aware machine learning for flow reconstruction”, funding from EPSRC
04/2021. Fully funded EPSRC-DTP PhD studentship in "Physics-aware machine learning for turbulence".
10/2020. Fully funded EPSRC-DTP PhD studentship in "Data assimilation in fluids".
03/2019. Fully funded EPSRC-DTP PhD studentship in "Prediction and control of extreme fluid dynamics with artificial intelligence".
05/2018. Fully funded post-doctoral position sponsored by the Hans Fischer fellowship on "Artificial intelligence algorithms in computational fluid dynamics".