Computational Materials Scientist​/Computational Chemist

We are seeking a part-time Computational Materials Scientist / Computational Chemist to help drive the experimentation and productization of innovative computational and machine learning approaches that accelerate the R&D and uptake of bio-based materials for net-positive impact.

• Location
  :
  London, UK
• Role Type
  :
  Part-time (0.4-0.6 FTE)
• Application Form
  : (Use the "Apply for this Job" box below)./

Materiom is an impact-focused tech startup with the mission to accelerate the research, development, and uptake of bio-based materials that have a net-positive impact on the planet. We do this by building datasets and software tools for scientists, producers, and brands. The Materiom Commons is our current platform, providing a large open database of material formulations and AI features to support a community of 20,000+ scientists, designers, engineers and entrepreneurs to quickly and easily find bio-based solutions for packaging and textiles applications.

We are evolving the platform through investments in data mining and predictive models, powered by new high-throughput experimental datasets from our data partners. Our interdisciplinary team blends deep expertise in circular economy, materials science, AI, and software development, and provides opportunities to learn from a diversity of perspectives. We’re creative optimists driven by a belief in collective action.

We are seeking a Computational Materials Scientist or Computational Chemist to join our R&D team and advance the discovery and development of next-generation sustainable materials. This role bridges polymer informatics, data science, and materials engineering
, with a strong emphasis on applying computational and machine learning approaches to solve real-world industry challenges.

• Develop and apply computational models to predict structure–property–performance relationships in polymers and composites.
• Use polymer informatics tools to analyze experimental and synthetic datasets, identifying trends and guiding experimental design.
• Integrate machine learning methods into materials discovery workflows to accelerate formulation and performance optimization.
• Identify gaps in knowledge and experimental capability which can be filled via simulation or model building.
• Develop strategies for handling data challenges, including the cleaning, featurization, and standardization of heterogeneous polymer datasets to improve the reliability of predictive models.
• Collaborate with experimental scientists and engineers to validate predictions through lab testing and pilot-scale trials.
• Translate insights from computational studies into actionable recommendations for industry applications (e.g., packaging films and coatings, textiles, rigid materials or building materials).
• Infuse model insights and artefacts into software products through close collaboration with the AI engineering team.
• Maintain and document computational workflows to ensure reproducibility and scalability across projects.

• PhD or MSc in Materials Science, Materials Engineering, Polymer Science, Chemistry, Chemical Engineering, Computational Chemistry
  , Computer Science or a related field.
• Strong background in polymer informatics
  , including experience with chemical representations and featurization strategies for polymers and formulations (e.g. (Big)
  SMILES-based descriptors, polymer abstractions, solubility and compatibility parameters such as Hansen solubility parameters), and a solid understanding of polymer chemistry and physics.
• Demonstrated proficiency in computational modeling and at least one programming language (e.g., Python (preferred), R, MATLAB).
• Experience working with machine learning techniques for materials data (supervised/unsupervised learning, feature engineering, predictive modeling).
• Track record of collaborating with industry or applying computational methods in industrial R&D contexts.

• Familiarity with high-throughput experimentation
  , robotic platforms
  , or self-driving labs
  .
• Experience integrating multi-scale data (molecular descriptors, MD simulations, made material properties) and…