(Senior) Computational Scientist, Biomolecular Simulation
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 simulation protocols, building both physics-based and machine-learning models, contributing to open-source software, and large-scale data analysis and curation.
Key Responsibilities
- Protocol Development
- Develop innovative simulation protocols to elucidate and quantify the binding interactions between small molecules and intrinsically disordered proteins
- Evaluate and advance methodologies for benchmarking and aligning simulations with experimental data
- Critically assess existing approaches using industry-leading benchmarking practices to identify areas for improvement
- Enhance the usability of simulation methods by implementing automated, streamlined, and efficient software solutions in line with best practices in software engineering.
- Model Development
- Employ cutting edge techniques–including machine-learned force fields, coarse graining, and enhanced sampling–to create new predictive models
- Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes
- Team Collaboration
- Collaborate closely with other computational team members and experimental biophysicists, incorporating experimental data into modeling and protocol development
- Mentor and support Bind’s interdisciplinary team in computational methods and experimental design
- Driving Innovation
- Stay current with breakthroughs in biomolecular simulation, deep learning, disordered proteins, and computational technologies
- Contribute to the design and execution of cutting-edge simulation-based research projects that advance Bind’s scientific mission.
Qualifications and Expertise
Education and Experience
- PhD in Physics, Chemistry, Biology, Computer Science, or a related discipline with experience in the modelling of (bio-)molecules or other complex systems.
- Extensive knowledge of simulation techniques such as molecular dynamics or Monte Carlo approaches, as well as a solid understanding of statistical mechanics and complex systems, and foundational knowledge of modern machine learning and deep learning techniques.
- Extensive experience in applying simulation and modelling techniques to solve complex (biophysical) problems, inform experiments, and make meaningful predictions.
- Track record of completed scientific software projects or open-source project contributions.
- Experience in combining simulation and (deep) machine learning approaches, such as through ML force fields, ML collective variables, or analysis methods based on ML.
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.
- 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
- Ability to use HPC and / or cloud computing and building automation and orchestration systems for these platforms.
- Experience in computational drug design and associated tooling such as docking, free energy perturbation, QSAR models, and basic cheminformatics approaches.
- Knowledge of coarse-graining approaches for biosimulation such as MARTINI, Gō models, or implicit solvation.
- Experience working with experimental biophysical data such as from Nuclear Magnetic Resonance Spectroscopy (NMR).
- Experience with simulation of intrinsically disordered proteins.
- Proficiency in a low-level language such as C, C++, or Rust.
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 simulation, machine learning, programming and a willingness to learn more about experimental 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!