PhD Studentship in Control and Machine Learning Algorithms for Autonomous Vehicles

Imperial College London
London, United Kingdom
Today
Job Type
Contract
Work Pattern
Full-time
Work Location
On-site
Seniority
Entry
Education
Phd
Posted
14 May 2026 (Today)

Benefits

Tuition fees covered Stipend at UKRI rate

Applications are invited for a research studentship in the field of control, leading to the award of a PhD degree. The studentship is fully funded by the Department of Mechanical Engineering, covering tuition fees and providing a stipend at the UKRI rate. It is available to UK (home) students only.

Autonomous driving technology is developing rapidly, and ensuring that next-generation systems operate reliably remains a key challenge, requiring new systematic approaches. Modern autonomous driving systems often rely on machine learning to determine high-level driving decisions from camera images, while separate control algorithms execute these decisions through steering and braking. However, the interaction between these components remains a key challenge. This project aims to develop new methods interfacing data-driven decision-making and model-based control modules for autonomous vehicles.

The PhD student will develop new algorithms for control of autonomous vehicles, with a focus on the interface between machine learning-based decision-making and control. This will involve designing and analysing novel algorithms, complemented by theoretical analysis and realistic simulations in open-source environments. The student will gain hands-on experience in algorithm design, simulation, and the integration of machine learning and control methods.

You will be an enthusiastic and self-motivated person who meets the academic requirements for enrolment for the PhD degree at Imperial College London. You will have a 1st class honours degree in mechanical engineering, computing, or a related subject. You take a rigorous approach to research and are motivated to tackle challenging problems.

Strong expertise in one of the following is required:

  • Machine learning, especially vision-based
  • Control systems, especially predictive control
  • Autonomous driving

Good programming skills (e.g., Python, MATLAB) are required. An interest in autonomous vehicles is essential. Good team-working and communication skills are important.

This PhD will be conducted within the Autonomous Systems group in the Department of Mechanical Engineering at Imperial College London (https://www.imperial.ac.uk/autonomous-systems).

For information on how to apply, go to:

http://www.imperial.ac.uk/mechanical-engineering/study/phd/how-to-apply/

Interested applicants should send an up-to-date curriculum vitae, a brief motivation letter related to this PhD, and contact details of one referee to Dr Johannes Kohler . Suitable candidates will be required to complete an electronic application form at Imperial College London in order for their qualifications to be addressed by College Registry.

Applications received by 15 June 2026 will receive full consideration. Applications submitted after this date will be reviewed on a rolling basis until the position is filled. The ideal start date is between September 2026 and February 2027 and can be discussed.

Related Jobs

View all jobs
Spotlight

Senior ML Compiler Engineer

Fractile Bristol, United Kingdom
Spotlight

Machine Learning Engineer - National Security (Gloucestershire)

Mind Foundry Gloucester, Gloucestershire, United Kingdom
On-site Clearance Required

PhD Studentship (m/f/x) in Geometric Deep Learning

HITS gGmbH Heidelberg - Germany, Hybrid, Europe, United Kingdom
€55 – €60 pa Hybrid

PhD Studentship: Machine Learning Accelerated Electronic Transport Calculations For Complex Materials

University of Warwick Coventry, University Of Warwick, Midlands Of England, United Kingdom
£21,805 pa On-site

PhD Studentship: Machine Learning for Organic Materials: From Molecules to Mobility

University of Warwick Coventry, University Of Warwick, Midlands Of England, United Kingdom
£21,805 pa On-site

PhD Studentship: Machine Learning Enhanced Muon Imaging for Nuclear Waste Monitoring and Safeguards

University of Surrey Guildford, South East England, United Kingdom
£26,000 pa On-site

PhD Studentship: Cracks and Code: From High-Fidelity Simulations to Fast Scientific Machine Learning Models

University of Warwick Coventry, University Of Warwick, Midlands Of England, United Kingdom
£21,805 pa On-site

PhD Studentship: From Brittle to Ductile: Machine Learning 3D Fracture Simulations for Extreme Environments

University of Warwick Coventry, University Of Warwick, Midlands Of England, United Kingdom
£21,805 pa On-site

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Machine Learning Jobs UK 2026: What to Expect Over the Next 3 Years

Machine learning has undergone a transformation that few technology disciplines can match. In the space of three years it has moved from a specialism sitting at the edges of most organisations' technology strategies to a capability that sits at the centre of them. The tools have changed, the expectations have shifted, and the range of industries treating machine learning as a core business function — rather than an experimental one — has expanded dramatically. For job seekers, this creates both opportunity and complexity in roughly equal measure. The machine learning jobs market of 2026 is significantly larger than it was three years ago, but it is also significantly more demanding. Employers have developed more sophisticated expectations, the technical bar for specialist roles has risen, and the landscape of tools, frameworks, and architectural patterns that practitioners are expected to know has broadened considerably. The candidates who will thrive over the next three years are those who understand where the discipline is heading — which specialisms are attracting the most investment, which technologies are reshaping what machine learning engineers and researchers are expected to build, and how the definition of a machine learning career is evolving beyond the model-building core toward a much wider range of roles across the full ML lifecycle. This article breaks down what the UK machine learning jobs market is likely to look like through to 2028 — covering the titles emerging right now, the technologies driving employer demand, the skills that will matter most, and how to position your career ahead of the curve.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.