Differential Bio - Machine Learning Data Scientist posting
Requirements
• PhD in machine learning, bioengineering, computational biology, or a related field, or a Master’s degree with 3+ years of relevant industry experience. • Strong research independence; ability to scope and execute novel scientific work from start to finish. • Demonstrated evidence of research contributions, including (but not limited to): peer-reviewed journal or conference papers, conference attendance, open-source contributions. • Strong Python and modern ML tooling (e.g., PyTorch/JAX, scikit-learn). • Hands-on experience building models of dynamical systems or time-series data, ideally combining mechanistic and data-driven approaches. • Experience working with frontier coding models, including multi-agent orchestration. • Clear written and verbal communication, including with non-ML partners. • Nice-to-have: • Experience with bioprocess or fermentation data, ideally from bioreactors at more than one scale • Experience with Raman, NIR, or other spectroscopic data and chemometrics (e.g., PLS) • Background in model-predictive control, reinforcement learning, or optimal control • Experience with transfer learning, domain adaptation, or representation learning on biological data • Familiarity with genome-scale metabolic models or kinetic modelling • Experience with Bayesian methods, uncertainty quantification, or design of experiments • Experience in collaborative or publicly funded research projects (e.g. Horizon Europe, Eurostars)
Responsibilities
• Hybrid bioprocess modelling • Develop hybrid mechanistic/ML cell response models that predict fermentation outcomes (growth, titer, yield, productivity) from process and sensor data. • Fuse time-series signals from process analytical technology (Raman, NIR, dielectric spectroscopy, dissolved oxygen, off-gas) with offline analytics. • Build mechanistic backbones (e.g., kinetic/ODE models, genome-scale metabolic models) that constrain learning and generate synthetic training data. • Develop machine learning surrogate models for computationally costly mechanistic simulations. • Quantify uncertainty so that models can be trusted in high-stakes production settings. • Transfer across organisms and scales • Design models that carry over from benchtop to pilot and production scale, and across various microbes including bacteria and yeast. • Use transfer learning and representation learning to make good predictions for new strains and processes with little data. • Develop scale-transferable reactor characterisations and predictive KPI models for scale-up. • Real-time Model-Predictive Control • Build the AI/ML layer for Nonlinear Model Predictive Control (NMPC) and reinforcement-learning-based feeding strategies to optimize titer, rate, and yield dynamically. • Fuse real-time data from Process Analytical Technology (PAT) and soft sensors (e.g., Raman, NIR) into dynamic state estimators to drive closed-loop bioreactor actuators (feed, air, agitation). • Prototype simpler, deployable steps along the way, such as deviation detection and operator alerts • Design control frameworks to proactively resolve metabolic bottlenecks and robustly account for model uncertainty, batch-to-batch variability, and inherent bioprocess stochasticity.
Benefits
• Competitive compensation through salary + stock options; we are looking to build a small but mighty team and think about compensation along those lines; • Build your research profile: Work on open problems with leading European academic and industrial partners, with the intention to present and publish your work; • A beautiful office in the heart of Munich, or remote work option with occasional travel • eGym WellPass membership
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