Data Scientist

Physicsx·Londonmid

In short

→Científico de datos en una empresa deep-tech que usa IA para simular sistemas físicos en ingeniería.
→Trabajas en modelos de aprendizaje profundo para problemas de física y geometría, colaborando con ingenieros y clientes.
→Destaca el enfoque en modelos avanzados como operadores neuronales y generativos para datos espaciotemporales y geométricos.

Fluent written and spoken English required for collaboration and communication with intern

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What they ask for

✓PhD en CS, ML, matemáticas, física o ingeniería con enfoque en ML aplicado a física.
✓Más de 2 años de experiencia industrial en roles data-driven con Python.
✓Experiencia con deep learning, especialmente en PDEs, geometría 3D o generativos.
✓Capacidad para formular problemas físicos como desafíos matemáticos y ML.
✓Habilidades sólidas de comunicación técnica para públicos académicos y no técnicos.
✓Experiencia en pipelines de experimentación, benchmarking y optimización de modelos.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonNumPySciPyPandasPyTorchJAXNeural OperatorsGeometric Deep LearningPoint-CloudMesh-Structured Data

Who should you write to at Physicsx?

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About us 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. Note: We are currently recruiting for multiple levels and positions, however please only apply for the role that best aligns with your skillset and career goals. 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. 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.). • >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 requir

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