You will lead the modeling and intelligence layer at Cultzyme, building the digital twins that optimize biopharmaceutical production. By coupling mechanistic kinetics with data-driven components like neural ODEs and PINNs, you’ll solve the predictability crisis in fermentation, directly impacting how life-saving therapies are manufactured and scaled globally.
Modelling Team Lead at Cultzyme
Cultzyme is revolutionizing biopharma manufacturing by building the world’s first "intelligent" bioreactors. As the Modelling Team Lead, you will lead the intelligence layer of the BION platform, developing hybrid mechanistic-ML digital twins that solve the industry's predictability crisis. This is a rare opportunity for a PhD-level computational scientist to apply neural ODEs and Bayesian optimization to real-world fermentation data, directly influencing the manufacturing of life-saving therapies. Join a high-talent, low-ego team of 15 in San Sebastián (or remotely) and take ownership of models that will define the future of biological production.
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Location
San Sebastián, Spain (Remote)
Compensation
€45k-€55k + Equity
Company
Cultzyme
Role overview
Biotechnology1-15 employees
Cultzyme is a deep-tech company specializing in the development of intelligent bioreactors and bioprocessing platforms that integrate advanced hardware, AI, cloud computing, and novel sensors to optimize cell growth, fermentation, and production of sustainable alternative proteins, biopharmaceutical therapies, and biosynthetic tissues.
What you will do
- Build hybrid cell growth models using neural ODEs, PINNs, and mechanistic kinetics to create high-fidelity digital twins of bioreactors.
- Design and execute DOE campaigns and Bayesian optimization loops to bridge the gap between intracellular metabolism and 2000L fluid dynamics.
- Develop real-time MLOps pipelines and PAT strategies to enable predictive control and automated process monitoring in regulated environments.
Who this is a fit for
- Holds a PhD in Mathematics, Physics, or Statistics with 5+ years of experience applying complex mathematical modeling to engineering problems.
- Deep expertise in hybrid mechanistic/data-driven modeling (ODE/DAE systems) and production-grade Python using libraries like PyTorch or JAX.
- Proven track record of building digital twins and closing the experimental loop with Bayesian optimization and Gaussian process surrogates.
Why this role is remarkable
- Impact: Your models will move beyond research papers into the actual manufacturing lines of the world’s leading pharmaceutical companies.
- Innovation: Work at the bleeding edge of hybrid modeling, combining first-principles physics with universal differential equations and Bayesian optimization.
- Ownership: As a core member of a high-talent 15-person team, you’ll have the autonomy to define the intelligence trajectory of the entire company.
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