Research Data Scientist

PhysicsX is a deep-tech company of scientists and engineers, developing machine learning applications to massively accelerate physics simulations and enable a new frontier of optimisation opportunities in design and engineering.

Born out of numerical physics and proven in Formula One, we help our customers radically improve their concepts and designs, transform their engineering processes and drive operational product performance. We do this in some of the most advanced and important industries of our time - including Space, Aerospace, Additive Manufacturing, Electric Vehicles, Motorsport, and Renewables. Our work creates positive impact for society, be it by improving the design of artificial hearts, reducing CO2 emissions from aircraft and road vehicles, and increasing the performance of wind turbines.

We are taking the next leap in building out our technology platform and product offering. In this context, we are looking for a capable and enthusiastic research data scientist to join our research team. If all of this sounds exciting to you, we would love to talk.

Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

What you will do

Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques.
Own Research work-streams at different levels, depending on seniority.
Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
Collaborate with colleagues beyond the research team to translate your models into production-ready code.
Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.
Foster a nurturing environment for colleagues with less experience in DS / ML / Stats for them to grow and you to mentor.
What you bring to the table
Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
Ability to scope and effectively deliver projects.
Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
Excellent collaboration and communication skills - with teams and customers alike.
PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
operator learning (neural operators), or other probabilistic methods for PDEs;
geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
Ideally, >2 years of experience in a data-driven role, with exposure to:
building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;
developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);
iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance;
combining theoretical reasoning with empirical intuition to guide investigation;
formulating and running experiment pipelines to benchmark models and produce comparable results;
writing skills for communication complex technical concepts to peers and non-peers, tailoring the message for the required audience.
Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.
What we offer
Be part of something larger: Make an impact and meaningfully shape an early-stage company. Work on some of the most exciting and important topics there are. Do something you can be proud of.
Work with a fun group of colleagues that support you, challenge you and help you grow. We come from many different backgrounds, but what we have in common is the desire to operate at the very top of our fields and solve truly challenging problems in science and engineering. If you are similarly capable, caring and driven, you'll find yourself at home here.
Experience a truly flat hierarchy. Voicing your ideas is not only welcome but encouraged, especially when they challenge the status quo.
Work sustainably, striking the right balance between work and personal life.
Receive a competitive compensation and equity package, in addition to plenty of perks such as generous vacation and parental leave, complimentary office food, as well as fun outings and events.
Opportunity to collaborate in our lovely Shoreditch office and enjoy a good proportion of time working from home, if desired. Get the opportunity to occasionally visit our customers' engineering sites and experience first-hand how our work is transforming their ways of working.
Use first-class equipment for working in-office or remotely, including HPC.
We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.

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