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Diffractive Labs

Autonomous AI research lab for materials discovery

Research Scientist - Machine Learning

AI ResearchConfirmed live in the last 24 hours
Full-timeLondon, UK

About the Job

Requirements

  • PhD in computer science, machine learning, physics, or a closely related field.
  • Proven track record of developing and evaluating novel machine learning methods, with a clear understanding of training dynamics and generalisation behaviour.
  • Strong Python skills and production-quality research code; experience with PyTorch or an equivalent machine learning framework.
  • Evidence of significant research impact through publications, open-source work, or applied research projects.
  • Comfort working in Linux-based environments, with version control using Git and HPC or cloud platforms.

Responsibilities

  • Develop new methods in modelling, reasoning, and experiment automation for an AI-driven material discovery engine.
  • Own the research direction for problems in this space by designing experiments, evaluating new approaches, and working with engineers to scale successful approaches onto real materials science problems.
  • Formulate and prototype novel machine learning architectures, including foundation models, graph neural networks, and generative models, tailored to materials science and chemistry.
  • Design active learning and optimization algorithms, such as Bayesian optimization or reinforcement learning, to decide which physical experiments an autonomous lab should run next.
  • Address research challenges in representation learning by devising ways to train effectively on sparse, noisy, high-dimensional data generated by real-world physical experiments.
  • Collaborate with materials scientists and chemists to translate physics, constraints, and scientific intuition into rigorous mathematical models and novel loss functions.
  • Drive research from theoretical ideation through proof-of-concept, establishing strong baselines and validating new algorithmic approaches before partnering with engineering to scale them.
  • Monitor machine learning literature, identify relevant techniques from adjacent fields, and adapt them to accelerate the discovery engine.

Desired Qualifications

  • Experience with scientific or simulation domains.
  • Experience applying machine learning in a scientific, simulation, or research computing setting.
  • Experience with large-scale or distributed training on GPU clusters.
  • Familiarity with scientific data formats and reproducibility practices.
  • Contributions to open-source machine learning or scientific computing packages.

About the Company

Diffractive Labs

Autonomous AI research lab for materials discovery

Diffractive Labs is a London-based company building an AI Material Scientist, an autonomous system that discovers new materials by learning from real-world experimentation. It pairs machine learning models, including graph neural networks, with robotic lab automation, so computational predictions and physical experiments continuously inform each other in a closed discovery loop. A team of experimental scientists, computational researchers, and platform engineers runs experiments at scale, replacing manual trial-and-error. What sets Diffractive Labs apart is closing the loop between simulation and lab work, letting AI models propose materials and validate them through automated experimentation. The goal is to accelerate materials discovery and expand the pace of scientific research.

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