Computational Materials Scientist (Materials Informatics / ML)

Company Description hAIrFiber™ is an early-stage, research-driven venture exploring how materials informatics and machine learning can be used to design next-generation synthetic fibres inspired by the structure and performance of human hair. The project sits at the intersection of materials science, polymer engineering, and AI, with a computational-first approach focused on data-driven property prediction and ML-guided materials screening. At this stage, the work is exploratory and research-oriented, aimed at building a strong technical foundation that could later support experimental validation and commercial development.

Role Description We are seeking a Computational Materials Scientist / Materials Informatics Researcher to support early-stage research into polymer-based fibre design. This role will focus on analysing existing materials datasets, building simple predictive models, and exploring structure–property relationships relevant to synthetic fibre performance (e.g. mechanical properties, thermal behaviour, durability). The work is computational and conceptual in nature rather than lab-based. The role is well suited to a postgraduate student, recent graduate, or early-career researcher interested in applying computational methods to real-world materials challenges in a startup-style research environment.

Key Responsibilities

• Curate and analyse polymer and materials property datasets
• Explore structure–property relationships using statistical and ML methods
• Build and evaluate simple predictive models for materials properties
• Support computational exploration of polymer compositions and fibre architectures
• Document findings clearly and communicate results to a non-specialist founder

Required Skills / Background

• Background in materials science, chemistry, chemical engineering, physics, or a related field
• Familiarity with Python and data analysis tools (e.g. NumPy, pandas, scikit-learn)
• Interest in materials informatics, computational chemistry, or ML for physical sciences
• Ability to work independently in an open-ended research environment

Nice to Have

• Exposure to polymer science or soft materials
• Experience with materials databases or property prediction
• Coursework or projects involving machine learning or modelling

Role Details

• Type: Part-time / Contract
• Location: Fully remote (UK-based)
• Compensation: Modest, appropriate for an early-stage research role (details discussed with shortlisted candidates)
• Visa sponsorship: Not available