Research Scientist

PhysicsX Ltd; City of Westminster; Hybrid; GBP 50,000 - 80,000; Full time

Job summary

A deep-tech company is seeking a machine learning researcher to work on AI-driven simulation software for engineering challenges. You will collaborate with engineers to develop predictive models and communicate findings. Ideal candidates have a PhD in relevant fields and experience in machine learning with strong problem-solving skills. This role offers a hybrid work setup and various employee benefits including equity options, flexible working, and comprehensive healthcare.

Benefits

Equity options

10% employer pension contribution

Free office lunches

Flexible working

Hybrid setup

Enhanced parental leave

Private healthcare

Personal development

Work from anywhere

Qualifications

• Enthusiasm for using machine learning in scientific contexts.
• Ability to deliver machine learning projects effectively.
• Experience in data-driven roles for >2 years.
• Publication record in reputable venues like NeurIPS or ICML.

Responsibilities

• Translate engineering challenges into mathematical problem formulations.
• Build predictive models for physical systems using machine learning.
• Collaborate to translate models into production-ready code.
• Communicate work results to peers and customers.

Skills

Machine learning

Deep learning

Problem-solving

Collaboration

Communication

Education

PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering

Tools

Python

NumPy

SciPy

Pandas

PyTorch

JAX

Job description

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.

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.

Qualifications

• 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.

Benefits

• 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.