Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

EPSRC ICASE Studentship in learning based hybrid PDE solver

University of Cambridge
Cambridge
10 months ago
Applications closed

Related Jobs

View all jobs

Research Associate in Physics-Informed Machine Learning for Crowd Dynamics

Research Associate in Physics-Informed Machine Learning for Crowd Dynamics

Research Associate in Physics-Informed Machine Learning for Crowd Dynamics

Users of Computer Aided Engineering applications always ask for higher computational speed and accuracy. Adopting Digital Twins broadly in the future, we expect this need to significantly increase. Recently, hybrid technologies - combining machine learning and classical simulation technologies - have been proposed to bring computational speed and accuracy of simulation tools to a new level. They thus have the potential to address user needs significantly better.

This PhD project seeks to explore a cutting-edge hybrid approach that combines machine learning with classical simulation methods to advance computational speed and accuracy. Specifically, the project will investigate the integration of Neural Operators-efficient learning-based partial differential equation (PDE) solvers defined on simplified domains (e.g., unit squares)-with domain decomposition strategies. This hybrid methodology aims to establish a new standard in simulation performance.

We invite applications from highly motivated individuals to join this project and contribute to this exciting area of research. Applicants should have (or expect to obtain by the start date) at least a high 2.1 degree (preferably a first or its equivalent) in Engineering, Machine learning, Applied Mathematics or related subject. This studentship is open to both home and overseas applicants. The successful candidate will work collaboratively with a multidisciplinary team based in Cambridge Universiy and Siemens Digital Industry Software, 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. EU and international students may be considered for a small number of awards at the Home fees rate. Full eligibility criteria can be found via the following link;

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

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

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

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.