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As a Research Scientist, you will develop machine learning models for engineering challenges, collaborate with teams, and mentor peers. You will also communicate your findings with various stakeholders and write for academic and industry audiences.
About us
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 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.
- 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.
- 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.
- Equity options – share in our success and growth.
- 10% employer pension contribution – invest in your future.
- Free office lunches – great food to fuel your workdays.
- Flexible working – balance your work and life in a way that works for you.
- Hybrid setup – enjoy our new Shoreditch office while keeping remote flexibility.
- Enhanced parental leave – support for life’s biggest milestones.
- Private healthcare – comprehensive coverage
- Personal development – access learning and training to help you grow.
- Work from anywhere – extend your remote setup to enjoy the sun or reconnect with loved ones.
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.
Top Skills
Jax
Numpy
Pandas
Python
PyTorch
Scipy
PhysicsX London, England Office
Victoria House 1 Leonard Circus, London, United Kingdom, EC2A 4DQ
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