Senior Manager, Physics Application Science
AQEMIA
We are seeking a Senior Manager to lead our Physics Application Science Team. This team is responsible for integrating and optimising physics-based computational methods to enhance our drug discovery efforts. This role is critical in ensuring that physics-based computational methods effectively contribute to our hit discovery and lead optimisation strategies, ultimately accelerating the identification of novel drug candidates. The ideal candidate will have both strong leadership abilities to manage a team of scientists and deep technical expertise in Statistical Mechanics and Computational Chemistry.
Key Responsibilities
- Lead, manage, and mentor a team of scientists specialising in physics-based computational methods.
- Define and execute strategic initiatives to integrate physics-based models, including our in-house state-of-the-art solvation free energy algorithm into the drug discovery workflow.
- Provide technical leadership in Statistical Mechanics, Molecular Dynamics, Free Energy Calculations, or Computational Chemistry.
- Collaborate closely with cross-functional teams, including AI researchers, medicinal chemists, and computational chemists, to align computational strategies with drug discovery objectives.
- Drive innovation by identifying and implementing emerging computational approaches to improve hit discovery and lead optimization.
- Ensure the team delivers high-quality scientific results on time, balancing research innovation with practical impact.
- Foster a collaborative and high-performing team culture, ensuring continuous growth and development of team members.
- Communicate scientific insights and strategic recommendations to senior leadership and key stakeholders.
Qualifications
- PhD in Statistical Mechanics, Computational Chemistry, Biophysics, or a related field.
- Senior experience in computational drug discovery, with at least 3 years in a leadership role.
- Strong expertise in physics-based computational methods, such as Molecular Dynamics, Free Energy Perturbation (FEP), or Enhanced Sampling Techniques with an emphasis on solvation free energy methods (using density functional theory, PBSA, GBSA for instance).
- Proven ability to manage, mentor, and develop a team of scientists in a high-performance environment.
- Strong project management skills with the ability to prioritize tasks and drive results.
- Excellent communication skills to engage with interdisciplinary teams and senior stakeholders.
- Familiarity with commercial drug discovery suites (e.g. Schrödinger and OpenEye), open source molecular dynamics packages (OpenMM, AMBER, GROMACS, NAMD) and chemoinformatics tools (e.g. RDKit and OpenBabel).
Nice to have:
- Experience in machine learning applications for physics-based modeling.
- Background in high-performance computing (HPC) and cloud-based simulations.
- Track record of scientific publications and contributions to the computational drug discovery community.
Preferred Mindset
- Ability to set a technical vision for the team and align it with company objectives.
- Collaborative Leader: Thrives in an interdisciplinary environment and values teamwork.
- Pragmatic and Impact-Driven: Balances scientific rigor with real-world application.
- Growth-Oriented: Invests in the development of team members and fosters a culture of continuous learning.
- Passionate about pushing the boundaries of physics-based drug discovery.