(Senior) Computational Scientist, Machine Learning

Location: Central London (Hybrid)

Type: Permanent (>4.5 years funding guaranteed)

Contact and application: careers@bindresearch.org (via the 'Apply' button above)

About Bind Research

Bind Research is an innovative not-for-profit research organisation at the forefront of developing tools and datasets to characterise small-molecule interactions with intrinsically disordered proteins. Based in central London, Bind leverages interdisciplinary methods that span cellular studies, experimental biophysics and computational approaches – with a strong focus on biomolecular simulation techniques combined with machine learning. You will play a crucial role in shaping the future of this cutting-edge research initiative from the beginning.

Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins using cutting-edge machine learning and simulation techniques. Whether you just graduated with some method-development experience or have multiple years of applying computational tools behind you, we encourage you to apply!

Role Overview

We are seeking a Scientist to advance computational and modelling capabilities at Bind. This role includes developing new machine-learning models, including developing new architectures, contributing to open-source software, and large-scale data analysis and curation.

Key Responsibilities

1. Model Development

• Develop innovative machine learning approaches to elucidate and quantify the interactions between small molecules and intrinsically disordered proteins.
• Integrate molecular simulations and deep learning approaches using cutting-edge architectures.

2. Software Engineering

• Enhance the usability of built models by implementing automated, streamlined, and efficient software solutions in line with best practices.
• Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes.

3. Team Collaboration

• Collaborate closely with other computational team members and experimental biophysicists, assisting with experimental data handling and curation.
• Assist in optimising data collection practices in both computational and experimental teams.
• Mentor and support Bind’s interdisciplinary team in machine-learning and data analysis methods.

4. Driving Innovation

• Stay current with breakthroughs in machine learning, neural networks, molecular simulation, and computational technologies.
• Contribute to the design and execution of cutting-edge machine learning and simulation research projects that advance Bind’s scientific mission.

Qualifications and Expertise

Education and Experience

• PhD in Math, Physics, Computer Science, Chemistry, Biology, or a related discipline with a machine learning or model-building focus.
• Extensive knowledge of machine learning approaches and neural network architectures, with a focus on generative models.
• Experience in applying machine learning and modelling techniques to graph-based data such as molecules and proteins, as well as time series.
• Track record of completed scientific software projects or open-source project contributions.

Skills and Abilities

• Strong written and verbal communication skills, with the ability to communicate effectively with team members in diverse fields.
• Strong programming abilities in Python, and extensive experience with the scientific and machine-learning stack: Numpy, Torch/Tensorflow/Jax, Scikit-learn, Scipy, Pandas.
• Expertise with deep learning approaches such as diffusion or flow matching.
• Proficiency in modern software development practices: code testing, documentation, packaging and deployment, version control using Git.
• Proven ability to process, analyse, and present large and complex datasets using techniques such as clustering and dimensionality reduction.

Nice-to-have

• Knowledge of simulation techniques such as molecular dynamics or Monte Carlo approaches, as well as an understanding of statistical mechanics and complex systems.
• Familiarity with nuclear magnetic resonance spectroscopy and associated data.
• Experience in combining simulation and (deep) machine learning approaches, such as through ML force fields, ML collective variables, or analysis methods based on ML.
• Ability to use HPC and / or cloud computing and building automation and orchestration systems for these platforms.
• Proficiency in a low-level language such as C, C++, or Rust.
• Competence in front-end web design to allow easy interfacing with large datasets.

Additional Attributes

• A strong engineering mindset – you believe ease-of-use, reproducibility, maintainability, and clear documentation are key requirements for scientific software and allow complex projects to gain results faster.
• Collaborative and interdisciplinary spirit with a strong willingness to engage in team-based research initiatives.
• Dedication to continuous professional development in machine learning, simulation, programming and a willingness to learn more about experimental biophysical methods.
• Passion for contributing to the establishment and growth of a world-class not-for-profit research organisation.

What we offer

• ⁠Industry-competitive salary
• ⁠Employer pension contribution in line with market standards
• ⁠30 days annual leave plus 8 bank holidays
• ⁠Additional benefits package

Our Culture

• Follow the science. We prioritise rigorous scientific inquiry, relying on evidence and expertise to guide decisions and actions, incorporating the latest research to achieve meaningful, ethical, and impactful outcomes for the public and scientific community.
• Think dynamically. We believe the most effective solutions come from a dynamic, adaptable mindset that embraces uncertainty as a catalyst for discovery, encouraging creativity, challenging assumptions, and approaching problems from multiple angles to foster innovation, navigate complexity, and deliver exceptional results.
• Celebrate a diverse ensemble. We celebrate diversity and inclusion, fostering a culture where all perspectives, backgrounds, and talents are valued, respected, and empowered to thrive, enabling us to better understand our community, collaborate effectively, and deliver impactful solutions.
• Build an innovation hub. We strive to advance disordered protein research by creating and sharing tools and datasets collaboratively, building on past contributions, and working alongside the disordered protein community to deepen understanding and maximise collective impact.

Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins!

Please apply by sending a CV to careers@bindresearch.org (via the 'Apply' button above) with a short covering paragraph outlining why you are interested in the role. We would like to keep your CV on file for future openings that align with your skills and experience. If you consent to us retaining your application for this purpose, please let us know in the covering email. You may request its removal at any time by contacting info@bindresearch.org.