EPSRC ICASE Studentship in Learning-Based Hybrid PDE Solver Opportunities

4 weeks ago


Cambridge, Cambridgeshire, United Kingdom University of Cambridge Full time

Unlocking Computational Speed and Accuracy Advancements

This PhD project aims to pioneer a cutting-edge hybrid approach, integrating machine learning with classical simulation methods to enhance computational speed and accuracy.

Key Objectives:

  • Investigate the integration of Neural Operators-efficient learning-based partial differential equation (PDE) solvers with domain decomposition strategies.
  • Develop a hybrid methodology to establish a new standard in simulation performance.

Requirements and Eligibility:

Applicants should possess a high 2.1 degree (preferably a first or its equivalent) in Engineering, Machine Learning, Applied Mathematics, or a related subject. Both home and overseas applicants are eligible, with EU and international students considered for a small number of awards at the Home fees rate.

Benefits and Support:

The successful candidate will work collaboratively with a multidisciplinary team, gaining expertise in advanced computational methods and state-of-the-art machine learning techniques. EPSRC ICASE studentships are fully-funded (fees and maintenance) for students eligible for Home fees.

Equality, Diversity, and Inclusion:

The University actively supports equality, diversity, and inclusion, encouraging applications from all sections of society.



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