PhD Studentship in Aeronautics: Real-time Data Assimilation for Multi-physics Flows (AE0095)

Imperial College London; Greater London; In loco; GBP 21.000 - 26.000; Tempo pieno

Vantaggi offerti da questo lavoro

Fully funded PhD studentship

Descrizione del lavoro

Imperial College London is offering a fully funded PhD studentship in Aeronautics to develop bias-aware data assimilation methods that correct model error as dynamics evolve. You will work at the interface of physics, mathematics and computation, using high-performance computing facilities and Bayesian inference techniques. The project spans 3.5 years, with supervision by Dr.

Andrea Nóvoa. A stipend of £23,805 per year (tax-free for Home, EU and International students) supports research and

Competenze

• First-class honours in Aeronautics, Mechanical Engineering, Mathematics, Statistics or Physics.
• Background in computational physics or mathematics is advantageous.
• Interest in data assimilation, dynamical systems and multi-physics computation at the physics-mathematics interface.

Mansioni

• Develop bias-aware data assimilation methods that learn from data as dynamics evolve.
• Apply Bayesian inference and machine learning within physics-constrained frameworks.
• Present work at international conferences and publish in peer-reviewed journals.

Conoscenze

Data assimilation

Bayesian inference

Physics-constrained ML

High-performance computing

Formazione

First-class honours MEng/MSci or international equivalent

Descrizione del lavoro

Start date:

Between 1st August 2027 and 1st July 2028

Application deadline:

23:59, 4th January 2027 (UK time)

Introduction:

Every model is wrong, and in engineering the errors matter. From aircraft wings to wind turbines and combustors, engineering systems are governed by fluid flows that interact with other physical processes, such as structural vibrations, acoustics and chemical reactions. On the one hand, physics-based models of these multi-physics systems are extrapolative – they provide predictions on phenomena that have not been observed. However, their parameters are uncertain, and their equations neglect part of the physics. On the other hand, measurements provide information on the true state of the system, but they are sparse and noisy. When operating conditions change rapidly, model errors grow, and forecasts lose accuracy precisely when they are needed most. Real-time data assimilation, which combines models with measurements, offers a way forward. However, most existing methods assume that the model is unbiased and they cannot cope with rapid changes in operating conditions, such as a gust hitting an aircraft wing or a sudden change in the load of a combustor. A fully-funded PhD studentship is available at the Department of Aeronautics, Imperial College London, to develop bias-aware data assimilation methods that estimate and correct model error as the dynamics evolve. The output of the project will be a model that learns from data any time that measurements are assimilated, without violating the physics. We will prototype the methods on low-order dynamical systems, and we will scale them up to higher-dimensional multi-physics problems.

You will gain experience in data assimilation and Bayesian inference, physics-constrained machine learning, and computational methods for multi-physics systems. You will employ high-performance computing facilities, present your work at international conferences, publish in peer-reviewed journals, and benefit from Imperial’s doctoral training programme and career support. These skills will prepare you for careers in academia, industry and research laboratories.

Supervisors:

Dr. Andrea Nóvoa

Duration:

3.5 years.

Funding:

Full tuition fees and an annual tax-free stipend of £23,805 for Home, EU and International students. Find out more about fee status.

Eligibility:

Candidates will be expected to achieve/have achieved a First class honours MEng/MSci or higher degree (or international equivalent) in: mathematical or engineering disciplines such as Aeronautics, Mechanical Engineering, Mathematics, Statistics or Physics.

A background in computational physics or mathematics, data assimilation, or dynamical systems for fluid mechanics is an advantage. We look for curious and motivated candidates who enjoy working at the interface between physics, mathematics and computation. We welcome applications from people of all backgrounds.

Contact:

For more specific project information, email: Dr. Andrea Nóvoa

For admissions questions, please first read our Frequently Asked Questions. If your question has not been answered, please email the PhD Administrator (Admissions)

Frequently Asked Questions

You can also find answers to common application questions on our Frequently Asked Questions.

Equality, Diversity and Inclusion:

Imperial is committed to equality and valuing diversity. We are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employerand are working in partnership with GIRES to promote respect for trans people.

£23,805 - please see advert