Numerical Methods Research Scientist (Scientific Computing)

About AQEMIA

AQEMIA is a drug invention company dedicated to creating entirely new medicines to address major unmet medical needs. At the core of our mission is QEMI, our proprietary molecule-invention platform, which uniquely combines cutting‑edge science with advanced technology. Powered by physics‑based modeling, statistical mechanics, and generative AI, QEMI allows our teams to design novel drug candidates from first principles.

About Our Team

AQEMIA brings together a diverse, multidisciplinary team of 65+ professionals based in Paris and London. Our scientists and engineers, including chemists, physicists, machine learning experts and software engineers, work side by side to push the boundaries of early‑stage drug discovery.

This close collaboration across disciplines is central to our approach, enabling us to tackle complex scientific challenges from first principles and translate cutting‑edge ideas into novel therapeutic candidates. At AQEMIA, team members are encouraged to contribute their expertise, learn from one another and play an active role in shaping the future of drug invention.

The Role

We are seeking a Numerical Methods Research Scientist to join AQEMIA’s R&D team, focused on the development, analysis and optimisation of numerical methods for physics‑based methods that accelerate our drug discovery platform.

What You’ll Do

• Develop, analyse and optimise numerical methods for the computation of binding and solvation free energies, with a focus on numeric aspects of the methods (code optimisation and/or algorithmic improvement).
• Implement the numerical methods to provide fast and efficient physics‑based algorithms such as:
  • Molecular Density Functional Theory (MDFT) and Classical Density Functional theory (CDFT)
  • Alchemical Solvation Free Energy (ASFE) methods
  • Other statistical mechanics‑based methods for binding and solvation free energy predictions.
  • Integration of machine learning and statistical mechanical methods in collaboration with ML specialists
• Create and perform method validation and benchmarking studies against experimental or high‑accuracy simulation data.
• Collaborate with fellow scientists across R&D, Platform, Engineering and Portfolio departments to develop methods and integrate them into production software.
• Stay current with scientific literature; contribute to bibliographic reviews and internal knowledge sharing.
• Clearly communicate progress through presentations, internal reports and written documentation.

What We’re Looking For

• PhD in Statistical Physics, Theoretical Chemistry, Computational Fluid Dynamics, Computational Mathematics, Numerical Analysis, Mechanical Engineering or any other field that involves large‑scale computing, numerical methods, etc. OR 6 years industrial experience in method development, numerics and code optimisation.
• Proven experience in numerical method development, implementation and code optimisation (for example with experience with numerical methods such as PDE solvers, optimisation algorithms, finite element/difference methods), evidenced by open source software packages, scientific publications and/or industrial projects.
• Strong foundation in numerical analysis (e.g., PDEs, optimisation, discretisation methods).
• Proficiency in scientific programming in Python and a lower‑level language such as C++, Fortran and/or GPU programming.
• Ability to rigorously read, implement and extend algorithms and methods from the literature, with a commitment to scientific rigor and structured problem‑solving in method development.
• Analytical, collaborative and solutions‑oriented mindset.
• Strong coding practices: clean, properly documented and tested code (unit testing, documentation, version control, collaboration with Git).
• Ability to work as part of a team based in both London and Paris.

Nice to Have

• Experience with high performance computing and parallelisation/vectorisation.
• Experience developing classical or electronic density functional theory methods.
• Experience with applying ML to computational methods.
• Background in chemical physics, statistical mechanics or molecular dynamics.
• Familiarity with atomistic modelling of proteins or other biochemical systems, or cheminformatics Python libraries (RDKit, Pandas, etc).
• Experience in a drug discovery environment.

We kindly request that all CVs be submitted in English.

Why Join Us?

At AQEMIA, we work for a mission: joining us means having your own impact on changing the way drugs are discovered, and helping to shape the direction of our fast‑growing company and team.

Expanding Drug Discovery Pipeline: Focused on critical therapeutic areas such as Oncology, CNS, Immuno‑inflammation, with in vivo proof of concept and patent‑stage programmes. Collaborations with top Pharma, including a $140M Sanofi deal.

World‑Class Interdisciplinary Team: work alongside exceptional talent at the intersection of technology and life sciences. Our teams combine deep expertise in AI, physics‑based modelling, biology and medicinal chemistry to push the boundaries of innovation.

DeepTech Recognition: AQEMIA is proud to be part of the French Tech 120 and France 2030, highlighting our role as a key player in Europe’s DeepTech ecosystem.

Prime Location with Flexibility: Our offices are located in the heart of Paris and London (King’s Cross), with flexible work arrangements including up to two remote days per week.

Strong Financial Backing: $100M raised from leading European and international investors.