3D Computer VisionGenerative ModelsGeometric Deep LearningMachine LearningOperator LearningPhysical Foundation ModelsPhysics AI
Useful technologies
JAXNumPypandasPythonPyTorchSciPy
Required qualifications
Field of studyApplied Statistics, Computer Science, Engineering, Machine learning, Mathematics, Physics (or related)
Study levelDoctorate
Original posting
About us
**Re-architecting Engineering for the Age of Intelligence
**
PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.
PhysicsX is starting a research team in Singapore to build physical foundation models alongside our customers and partners, targeting engineering domains where this capability will be most transformative.
What you will do
Work closely with our machine learning engineers, simulation engineers, customers and partners 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 techniques that scale to large datasets, iterating through robust experimentation.
Chart a path through competing trade-offs with insufficient information, e.g. is it better to train a bigger model or to generate more data?
Own Research work-streams at different levels, depending on seniority.
Discuss the results and implications of your work with colleagues and customers, connecting with real-world problems.
Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc.
Foster curiosity and initiative among your colleagues and mentees.
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 communicating 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, TPAMI/JMLR, Nature and Science.
What we offer
Build what actually matters
Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.
Learn alongside exceptional people
Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.
Influence over hierarchy
We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.
Sustainable pace, long-term ambition
Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.
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. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.