Internship - ADC modeling (m/f/d)

Start Date: 1st January 2027

Duration: 6 months

Localisation: Corsier-sur-Vevey

Role Level: Internship

Conjugation of monoclonal antibody with a drug linker is a critical step in the development of antibody-drug bioconjugates. Today, offline analytical methods provide detailed information on the reaction, but they are time-consuming and don’t enable continuous process monitoring.The objective of this project is to develop a proof of concept for a hybrid soft sensor combining UV-Vis spectroscopy to provide fast, information-rich process measurements; a kinetic reaction model to describe the evolution of the conjugation species; an Extended Kalman Filter to combine measurements and model predictions while accounting for their respective uncertainties. The internship will focus primarily on modelling and data analysis, with experimental work performed when needed to support the project.

Your Role:

• Support the development and evaluation of the UV-Vis-based soft sensor, including data preprocessing and chemometric data treatment.
• Develop kinetic reaction models using gPROMS and/or Python to describe the evolution of the conjugation species.Compare different kinetic model structures, estimate kinetic parameters, and assess the predictive performance.
• Validate the kinetic model against independent experimental or offline analytical data where available.Combine kinetic model with the soft sensor data using an Extended Kalman Filter.
• Evaluate the ability of the hybrid model to update the estimated reaction state and predict the reaction progress.Identify data gaps and, if required, design and perform additional small-scale conjugation experiments.
• Analyze model performance, robustness and limitations.
• Plan and execute experiments in collaboration with PAT, modelling and process experts.Ensure that experimental conditions and results are accurately recorded and traceable.
• Develop Python-based tools for data preparation, visualization, and analysis.Present your results during project reviews and prepare a final technical report.

Your Profil:

• Currently in master’s or Engineering student in data science, analytical chemistry, biochemistry, biotechnology, chemical engineering or a related field.
• Strong interest in reaction kinetics, mechanistic modelling, dynamic systems, and data science.
• Experience with python and gPROMS or another process modelling tool is a strong plus.
• Interest in spectroscopy and chemometrics; knowledge of UV-Vis or PLS is an advantage.
• Practical laboratory experience and strong attention to data quality and reproducibility.
• Knowledge of Kalman filtering is a plus.
• Curious, rigorous, proactive, and able to work independently as well as in a
• multidisciplinary team.
• Fluent in English