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PhysicsX

Physics AI platform for industrial engineering

Senior Research Scientist

AI ResearchPosted on Nov 11
Full-timeLondon, UK

About the Job

Requirements

  • Ability to scope and effectively deliver projects.
  • Enthusiasm for using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
  • Strong problem-solving skills and the ability to analyze issues, identify causes, and recommend solutions quickly.
  • PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in operator learning (neural operators) or other probabilistic methods for partial differential equations; geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; or generative models for geometry and spatiotemporal data, including variational autoencoders, diffusion models, Bayesian non-parametric methods, and scaling to large datasets.
  • Ideally, more than four years of experience in a data-driven professional industry role, including building machine learning models and pipelines in Python using common libraries and frameworks such as PyTorch and CUDA, ideally with exposure to JAX, NumPy, and SciPy, especially for deep learning applications.
  • Ideally, experience developing models for bespoke problem settings involving high-dimensional spatiotemporal, geometric, or physical data.
  • Ideally, experience iterating on network architectures and model structure, tuning and optimizing for inductive biases, generalizability, and performance.
  • Ideally, experience combining theoretical reasoning with empirical intuition to guide investigations.
  • Ideally, experience formulating and running experiment pipelines to benchmark models and produce comparable results.
  • Ideally, writing skills for communicating complex technical concepts to peers and non-peers, tailoring the message to the audience.
  • Publication record in reputable venues demonstrating mastery in the field and relevant domains, with desirable venues including NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, SIGGRAPH, CVPR, or TPAMI/JMLR.

Responsibilities

  • Own research work streams at a high level to deliver outcomes.
  • Align priorities with internal and external problem stakeholders.
  • Set the technical direction for the research stream and apply judgment to drive progress.
  • Plan roadmaps with clear milestones for key decisions and outcomes.
  • Organize and guide junior team members to execute and deliver against the roadmap.
  • Communicate the purpose and key outcomes of research to raise awareness across the company and create opportunities for use and deployment.
  • Contribute to research group strategy and culture by identifying valuable research areas, championing their development, and prioritizing them against other research objectives.
  • Promote effective working patterns and proactively flag team-dynamic issues to foster a productive environment.
  • Nurture colleagues and guide their professional development.
  • Work with machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
  • Build models to predict physical-system behavior using machine learning and deep learning techniques.
  • Discuss research results and implications with colleagues and customers, including how the results can address real-world problems.
  • Collaborate with colleagues beyond the research team to translate models into production-ready code.
  • Communicate work internally and externally through paper publication venues, industry workshops, and customer conversations, including writing for academic and non-academic audiences.

About the Company

PhysicsX

Physics AI platform for industrial engineering

PhysicsX uses physics-based artificial intelligence for industrial engineering, combining machine learning with physics simulations to shorten design and operation cycles for aerospace, energy, and automotive sectors. Its platform replaces multi-day simulations with near-real-time models that predict performance, helping teams test ideas faster while reducing emissions and costs. It differs from competitors by delivering physics-driven AI with fast, scalable simulations tailored to large, complex industrial systems, supported by a slate of major investors and industrial partners. The company aims to speed up product development and drive broad adoption of physics AI across industrial sectors.

Company size
201-500
Company stage
Series C
Total funding
$487M
Headquarters
London, United Kingdom
Founded
2019

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