Computational Scientist - Molecular Modelling, API, DFT, kMC - Hybrid

Computational Scientist - Molecular Modelling, API, DFT, kMC - Hybrid, UK

A large global organization are looking for a Computational Scientist (Molecular Modelling) to lead a specialized project aimed at bridging the gap between theoretical simulations and practical experimental validation for API solid-state stability.

This will be a 12 months contract with the possibility for extensions.

The role will be worked on a hybrid/remote basis from the UK.

Key Responsibilities/Objectives:

• Establish a quantifiable formulability score or classification system for API solid forms to minimize instability risks and support the development of small-molecule pharmaceutical products.

• Development and validation of advanced computational models using first-principles and mesoscale simulation methods (DFT, phonon calculations, lattice dynamics, kinetic Monte Carlo).
• Rigorous comparison of simulation results with experimental data.
• Actionable insights to guide process optimization and solid-form selection.

• Develop, optimize, and execute integrated computational simulations
• First-principles methods: Density Functional Theory (DFT), phonon calculations, lattice dynamics
• Mesoscale models: Kinetic Monte Carlo (kMC)
• Build models addressing both intrinsic crystal stability and process-condition stability
• Ensure reproducible computational environments, including code, documentation, and configuration files.

• Produce a complete, validated dataset of simulation outputs covering intrinsic and process-related stability aspects.
• Perform systematic validation against available experimental data.
• Ensure data consistency, compatibility, and traceability.

• Deliver detailed analytical reports on model methodology, findings, and interpretations.
• Develop a formulability score or ranking framework to assess API developability.
• Provide scientific insights that support process optimization and solid-form selection.

• Build and document scripts, tools, and prototype pipelines for data processing, integration, transformation, and validation.
• Support future automation by internal teams while not being responsible for full deployment.

• Participate in structured governance with defined control gates:
• Initial model setup & validation
• Mid-project review & go/no-go decisions
• Final validation & project review
• Communicate progress, risks, and adjustments to simulation methods or data workflows.