Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
About the Role
PhysicsX is seeking individuals to join their team in building an AI-driven simulation software stack for engineering and manufacturing. This role involves translating physics and engineering challenges into mathematical formulations, building predictive models using machine learning, and contributing to research work-streams. The position requires discussing results with colleagues and customers, collaborating on production code, and communicating work through publications and presentations. Mentorship of junior colleagues is also expected.
Responsibilities
- Work closely with 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.
- Foster a nurturing environment for colleagues with less experience in DS / ML / Stats for them to grow and you to mentor.
Requirements
- 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.).
-
2 years of experience in a data-driven role in a professional industry setting (excluding post-doc positions), 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.
Skills
- Machine learning
- Deep learning
- Probabilistic methods
- Python
- NumPy
- SciPy
- Pandas
- PyTorch
- JAX
- Operator learning
- Neural operators
- Probabilistic methods for PDEs
- Geometric deep learning
- 3D computer vision
- Point-cloud data
- Mesh-structured data
- Generative models
- Geometry data
- Spatiotemporal data
- VAEs
- Diffusion Models
- Bayesian non-parametric
- High-dimensional data
- Network architectures
- Model structure
- Inductive biases
- Generalisability
- Performance optimisation
- Theoretical reasoning
- Empirical intuition
- Experiment pipelines
- Model benchmarking
- Academic writing
- Non-academic writing
Location
- Shoreditch office
Work Type
- Hybrid
Experience Level
-
2 years of experience in a data-driven role in a professional industry setting (excluding post-doc positions)
Education Level
- PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field
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 a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.
- We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries.
- By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility.
- Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.
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 UK employment and equality legislation.
- This information is confidential, used only in aggregate form, and will not influence the outcome of your application.