Why leave NVIDIA to join Mistral?

In this episode, Neil Ashton explains his move to Mistral as VP of Computational Engineering, where he will work alongside Johannes Brandstetter on AI for engineering, physics foundation models and large language models.

Neil reflects on the career decisions that took him from Manchester and Formula 1 to Oxford, NASA collaborations, AWS and NVIDIA. He shares what he learned from moving between academia and industry, how open datasets became central to his work, and why the chance to keep learning and build something new made Mistral an exciting next step.

For students, researchers and engineers considering a change, the episode also offers a personal perspective on developing a specialism, keeping an academic mindset in industry, and staying open to opportunities without needing to plan your entire career in advance.

Topics include:

• Getting a first break in Formula 1
• Switching between academia and industry
• Learning HPC, cloud computing and product engineering
• Machine learning, open datasets and scientific collaboration
• NVIDIA's hardware, software and partnership ecosystem
• Why Mistral, and working with Johannes Brandstetter
• Career advice: expertise, continual learning and embracing change

Podcast archive: https://neilashton.co.uk/podcasts/

Chapters:
00:00 Podcast intro
00:39 From NVIDIA to Mistral
01:46 My new role: physics AI and LLMs
03:18 Starting out in engineering and motorsport
06:26 A PhD and finding a specialism
08:38 Getting a first break in Formula 1
09:52 Returning to academia
11:57 Oxford, HPC and NASA
15:21 Consulting, Formula 1 and the connection to AWS
19:39 Leaving Oxford and joining AWS
21:06 Product engineering, ML and open datasets
23:29 What I learned at NVIDIA
26:39 Why Mistral felt like the next step
29:51 Career advice: stay open to change
31:28 Becoming known for a specialism
32:41 Podcast plans and closing thoughts

Paper mentioned:
Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
Neil Ashton, Johannes Brandstetter and Siddhartha Mishra
https://arxiv.org/abs/2511.20455

These are personal reflections on my own career and experience.

Should you still study engineering when AI can already write code, analyse data and automate parts of an engineer's job?

In this solo episode, Neil Ashton gives his view on engineering education and careers in the age of AI. His answer is yes—but the skill set is changing. Neil explains why engineering fundamentals still matter, where AI can act as an enabler, what students and early-career engineers should learn now, and why soft skills, projects and internships may become even more important.

Topics include:

- Why demand for engineers is likely to remain strong

- The engineering tasks most likely to change

- AI as an enabler for coding, CAD, CAE and automation

- Why domain knowledge is still essential for checking AI's work

- What practical AI fluency means beyond using a chat interface

- Advice for undergraduate, postgraduate and PhD students

- How projects, internships and soft skills can help you stand out

Podcast archive: https://neilashton.co.uk/podcasts/

Chapters:

00:00 Podcast intro

00:39 The career question in the age of AI

03:20 Why engineering demand is still growing

04:43 Which engineering tasks AI will change

05:21 AI as an engineering enabler

09:18 Why fundamentals and domain expertise still matter

11:59 AI fluency and the hiring market

16:41 Advice for students and researchers

20:05 What engineers should study now

22:11 Standing out: soft skills, projects and internships

25:41 Is engineering still worth it?

Resource mentioned:

- World Economic Forum, Future of Jobs Report 2025 — Skills outlook: https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/3-skills-outlook/

Please note that this episode expresses my personal opinion and does not represent the views of NVIDIA.

Hydrogen combustion, high-fidelity CFD and the future of aircraft propulsion are the focus of this conversation with Dr. Daniel Mira, Head of the Propulsion Technologies Group at the Barcelona Supercomputing Center. Neil and Dani discuss why reacting flows are so difficult to simulate, how hydrogen changes combustion and aircraft design, the limits of RANS, LES and DNS, GPU-native solvers, coding agents and AI surrogate models.

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s4-e6-daniel-mira-on-hydrogen-combustion-modelling-and-future-propulsion/

Topics

Why reacting flows are so computationally difficult

Hydrogen versus hydrocarbon combustion

When hydrogen could reach commercial aviation

How engines and aircraft must be redesigned

Industrial trust in high-fidelity combustion CFD

RANS, LES and DNS for reacting flows

Chemistry, load balancing and computational cost

Wall modelling in combustion LES

GPU acceleration and solver redesign

Coding agents for scientific software

AI surrogate models and digital engineering workflows

Selected resources

Daniel Mira and the Propulsion Technologies Group

https://ptg.bsc.es/?p=44

Propulsion Technologies Group — research lines

https://ptg.bsc.es/research-lines/

BSC — Combustion research

https://www.bsc.es/research-development/research-areas/engineering-simulations/combustion

Center of Excellence in Combustion (CoEC)

https://coec-project.eu/

High-fidelity simulations of the mixing and combustion of a technically premixed hydrogen flame

https://upcommons.upc.edu/entities/publication/08a27c10-cb13-4357-a3ab-8e9ec1d706cc

Chapters

00:00 Podcast intro

00:39 Introducing Daniel Mira

03:00 Conversation begins

04:55 Why combustion CFD is so hard

10:23 Daniel’s path into hydrogen and jet-engine combustion

12:48 Hydrogen versus hydrocarbon combustion

17:58 Industrial adoption of hydrogen

20:54 Gas turbines, aviation and fuel infrastructure

25:35 How jet engines must change

30:43 Redesigning the whole aircraft

34:46 What will trigger commercial adoption?

37:27 Why aerospace projects take a decade

42:14 RANS, LES and DNS for reacting flows

44:31 Replacing expensive tests with high-fidelity CFD

46:01 The biggest accuracy gaps in combustion LES

49:26 Where the computational cost goes

52:06 Chemistry, species and source-term bottlenecks

55:35 Wall modelling in combustion LES

59:49 GPUs, algorithms and solver redesign

01:08:52 Can coding agents accelerate combustion CFD?

01:12:27 AI surrogate models for combustion

01:24:20 Closing thoughts