Computational Scientist - biomolecular machine learning
Job Title: Bimolecular Machine Learning Scientist
Salary: Competitive, based on experience £80,000 - £120,000
Employment Type: Full-time
Start Date: ASAP
Benefits Package:
* Cash Back Health Plan
* Group Income Protection
* Group Life Insurance
* Modern Workspace: Access to WeWork offices
About the Role:
We are seeking a Computational Scientist specializing in machine learning and physical simulations to help design next-generation genetic therapies. You will develop molecular dynamics simulations to advance DNA-modifying enzymes and biologic delivery systems, accelerating drug discovery through GPU-accelerated machine learning models. Your work will bridge structural biology, artificial intelligence, and biophysics, contributing to life-changing therapeutics.
Key Responsibilities:
* Develop molecular dynamics simulations for proteins, peptides, and macromolecular assemblies.
* Apply machine learning models (e.g., graph neural networks, diffusion models) to improve simulation accuracy and efficiency.
* Implement uncertainty quantification and model interpretability techniques for scientific simulations.
* Collaborate with software engineers, MLOps engineers, and scientists to scale ML models and simulations.
* Optimize models for high-performance computing (HPC) environments and GPU acceleration.
* Stay up to date with advances in AI, biophysics, and molecular simulations.
* Communicate research findings through reports, presentations, and scientific publications.
Required Qualifications:
* Ph.D. in Physics, Computer Science, Computational Chemistry, Applied Mathematics, or a related field with expertise in machine learning and simulations.
* Experience in developing ML models for scientific applications.
* Strong knowledge of ML fundamentals (linear algebra, optimization, probability, statistics).
* Proficiency in Python and ML libraries like PyTorch, JAX, TensorFlow.
* Experience with molecular simulation software (e.g., LAMMPS, GROMACS, NAMD, COMSOL).
* Understanding of full ML lifecycle, including data processing, model training, deployment, and monitoring.
Preferred Skills:
* Familiarity with HPC environments and Nvidia GPU acceleration.
* Experience with cloud computing platforms and containerization tools (Docker, Kubernetes).
* Application of generative models, active learning, or reinforcement learning in scientific computing.
* Contributions to open-source projects in ML or scientific computing.
* Background in drug discovery, materials science, or gene therapy applications.
This is a rare opportunity to work at the cutting edge of AI-driven therapeutic design, leveraging machine learning and molecular simulations to shape the future of precision medicine.
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