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About the Role
You will work at the frontier of 3D geometry representation and generation, focusing on making both ready for engineering. You will also work on closing the coupling between geometry and physics, towards generating shapes driven by physical objectives.
Responsibilities
- Own a research work-stream: set its technical direction, align priorities with internal and external stakeholders, and apply judgement and taste to drive progress.
- Design and train generative models over 3D geometry — representations, latent spaces, and the objectives that decide whether generated geometry is physically usable rather than merely plausible.
- Work against production numerical solvers, so model quality is measured in validated physics rather than proxy metrics.
- Work with our ML engineers, simulation engineers and customers to turn engineering challenges into mathematical formulations.
- Champion research directions valuable to the company, nurture more junior colleagues, and publish for both academic and non-academic audiences.
Requirements
- PhD in CS, ML, mathematics, physics, engineering or a related field, with demonstrated expertise in generative modelling or learned representations of 3D geometry.
- Relevant experience in industry or a research group of comparable intensity, instrumental in building models and pipelines in PyTorch/CUDA.
- Experience with bespoke problem settings involving geometric or 3D data.
- Experience iterating on architecture and inductive bias.
- Experience combining theoretical reasoning with empirical intuition.
- Experience running experiment pipelines that produce comparable results.
- Ability to scope and deliver projects, with strong problem-solving skills.
- Enthusiasm for deep learning and probabilistic methods applied to science and engineering.
- Publications at premier conferences: SIGGRAPH, SIGGRAPH Asia, CVPR, ICCV, ECCV, NeurIPS, ICML or ICLR, and relevant journals: TOG, TPAMI, JMLR.
Skills
- Generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets
- Geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention
- Neural fields and shape autoencoders
- Operator learning or probabilistic methods for PDEs (Desirable)
- Self-supervised pretraining for 3D vision and/or physics surrogacy (Desirable)
- Generative inverse design linking geometry to physics objectives (Desirable)
Location
- United Kingdom
- London
- New York
- Singapore
- Bay Area
Work Type
- Hybrid
Experience Level
- Multiple levels and positions
Education Level
- PhD
Benefits
- Equity options
- 10% employer pension contribution
- Free office lunches
- Enhanced parental leave (3 months full pay paternity and 6 months full pay maternity leave)
- YellowNest nursery scheme
- 25 days of Annual Leave (+ Public Holidays)
- Private medical insurance (100% employee cover)
- Wellhub Subscription
- Eye tests
- Personal development support
- Employee Assistance Programme (EAP)
- Bike2Work scheme
- Season ticket loan
- Octopus EV salary sacrifice
About the Company
- 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.
Equal Opportunity
- 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.