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. Our core platform, QEMI, combines cutting‑edge science with advanced technology powered by physics-based modeling, statistical mechanics, and generative AI to design novel drug candidates from first principles. We focus on inventing never-before-seen molecules, advancing them into a growing pipeline of proprietary programmes, and forming strategic partnerships with leading pharmaceutical companies. Our most advanced preclinical programmes are currently in vivo optimisation, targeting diseases that still lack effective treatments. AQEMIA brings together a diverse, multidisciplinary team of 65+ professionals based in Paris and London. Our scientists and engineers work side by side to push the boundaries of early‑stage drug discovery.

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, focusing 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.

What We’re Looking For

• PhD in Statistical Physics, Theoretical Chemistry, Computational Fluid Dynamics, Computational Mathematics, Numerical Analysis, Mechanical Engineering, or a related field involving large‑scale computing and numerical methods; or 6 years of industrial experience in method development, numerics and code optimisation.
• Proven experience in numerical method development, implementation and code optimisation (e.g., PDE solvers, optimisation algorithms, finite‑element or finite‑difference methods), evidenced by open‑source software, scientific publications 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 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.
• 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 applying ML to computational methods.
• Background in chemical physics, statistical mechanics or molecular dynamics.
• Familiarity with atomistic modelling of proteins or other biochemical systems, and cheminformatics Python libraries (RDKit, Pandas, etc).
• Experience in a drug‑discovery environment.

Why Join Us?

• Expanding Drug Discovery Pipeline: focused on critical therapeutic areas such as Oncology, CNS and Immuno‑inflammation, with in vivo proof‑of‑concept and patent‑stage programmes and collaborations with top pharma.
• World‑Class Interdisciplinary Team: work alongside exceptional talent at the intersection of technology and life sciences.
• DeepTech Recognition: proud member of French Tech 120 and France 2030.
• Prime Location with Flexibility: offices 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: $100 M raised from leading European and international investors.

All CVs must be submitted in English.