Fully-funded PhD positions and post-docs
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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.
Job opening (deadline 4 Feb 2024): Fully-funded 12-month research assistant/postdoctoral position in Real-time scientific machine learning (computations and theory). Applications can only be considered when submitted through the Imperial portal: https://www.imperial.ac.uk/jobs/description/ENG02941/research-assistant-associate-real-time-scientific-machine-learning-computations-and-theory/ Job opening (deadline 18 Dec 2023): Fully-funded 24-month postdoctoral position in Scientific machine learning and digital twins for extreme fluids in propulsion. Applications can only be considered when submitted through the Politecnico di Torino portal: https://careers.polito.it/default.aspx?id=322/2023-AR Job opening (deadline 18 Dec 2023): Fully-funded 12-month postdoctoral position in Scientific machine learning and digital twins for extreme fluids in propulsion. Applications can only be considered when submitted through the Politecnico di Torino portal: https://careers.polito.it/default.aspx?id=321/2023-AR Job opening (deadline 8 Dec 2023): Fully-funded 24-month Research Assistant / Associate in Scientific Machine Learning. Applications can only be considered when submitted through the Imperial portal: https://www.imperial.ac.uk/jobs/description/ENG02850/research-assistant-associate-scientific-machine-learning/ Current funding (EU YoungResearcher): Real-time digital twin for thermo-fluid mechanics ERC-PI_0000005 Current funding (UKRI AI for Net Zero): Real-time digital optimisation and decision making for energy and transport systems EP/Y005619/1. Current funding (UKRI New Horizon): Can quantum algorithms revolutionise the simulation of turbulent flows? https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/X017249/1 Current funding (ERC Starting Grant): Physics-constrained adaptive learning for multi-physics optimization https://cordis.europa.eu/project/id/949388 Current funding (UKRI ExCalibur): Turbulence at the exascale: application to wind energy, green aviation, air quality and net-zero combustion Exascale Computing Grants |
Past fully funded positions
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06/2023. Two fully funded postdoctoral positions in Real-time digital optimisation and decision making for energy
11/2022. One fully-funded postdoctoral position in Quantum algorithms for the simulation of turbulent flows. 06/2022. 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 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". |