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.

What makes AQEMIA different is our commitment to true innovation: our research is dedicated to the invention of new molecular entities, not the refinement of existing ones. We focus on inventing never-before-seen molecules , without relying on experimental data, and advancing them into a growing pipeline of proprietary programs and strategic partnerships with leading pharmaceutical companies.

Our most advanced preclinical programs are currently in vivo optimization , targeting diseases still waiting for effective treatments, offering our teams the opportunity to work on science that can make a real difference in people’s lives.

For more information, visit AQEMIA.com and our LinkedIn.

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 optimization of numerical methods for physics-based methods that accelerate our drug discovery platform.

You will design, implement, improve and validate numerical methods for physical chemistry and statistical mechanics applications. This role sits at the intersection of numerical analysis, statistical physics, and high-performance scientific computing. You will work closely with research scientists, applied physicists, computational chemists, AI researchers, and data scientists, contributing directly to the advancement of our core physics engine, a critical component of Aqemia’s drug discovery platform.

What You’ll Do

• Develop, analyze and optimize numerical methods for the computation of binding and solvation free energies, with a focus on numeric aspects of the methods (code optimization 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

Note that this is not an ML-focused role.

• Create and perform method validation and benchmarking studies against experimental or high-accuracy simulation data.
• Collaborate with fellow scientists across R&D, Platform, Engineering, 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 optimization
• Proven experience in numerical method development, implementation and code optimization (for example with experience with numerical methods such as PDE solvers, optimization algorithms, finite element/difference methods), evidenced by open source software packages, scientific publications and/or industrial projects.

Nice to have

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

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 like Oncology, CNS, Immuno-inflammation... with in vivo proof of concept/patent stage programs. 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 modeling, 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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.