Internship - Hybrid modelling for predicting stability of monoclonal antibody in different pH(m/f/d)

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Ready to explore, break barriers, and discover more? We know you’ve got big plans – so do we! Our colleagues across the globe love innovating with science and technology to enrich people’s lives with our solutions in Healthcare, Life Science, and Electronics. Together, we dream big and are passionate about caring for our rich mix of people, customers, patients, and planet. That's why we are always looking for curious minds that see themselves imagining the unimaginable with us.

Everything we do in Electronics is to help us deliver on our purpose of being the company behind the companies, advancing digital living. We are dedicated to being the trusted supplier of high-tech materials, services and specialty chemicals for the electronics, automotive and cosmetics industries. We foster a global collaborative organization made up of individuals who have the passion to win, obsess about the customer, are relentlessly curious and act with urgency. Together, we push the boundaries of science to make more possible for our customers.

Start date : 01.03.2025

Duration : 6 months 

Location : Corsier-sur-Vevey

Role : Internship

Your role: 

In biopharmaceutical industry, the drug modalities that is explored in the pipelines are not limited to monoclonal antibody, and bispecific antibody is gaining popularity. These bispecific monoclonal antibody brings additional molecular complexity, leading to need for additional experiments to understand the molecule behavior in different processing conditions. Within Merck, we have decades of historical knowledge in developing bioprocesses for monoclonal antibody. The information that we gather from the process development can  be used to build quantitative structure-property relationships (QSPR). These relationships are mathematical expressions linking the specific characteristic of a molecule to the process set points.

This internship will be focused on development of QSPR model for predicting the antibody stability in different pH conditions. 

The outcome of the internship will be testing the different statistical models for building predictive hybrid models. Python will be used to develop the models and validate the models using new molecule data. Different model evaluation parameters will be compared to judge the applicability of the models for new molecule prediction. 

Your profil: 

• Master in Chemical engineering, Biotech engineering, Biostatistics.
• Experience working with data / mechanistic models would be ideal
• Python, data modelling (machine learning models)
• Language : English

What we offer: We are curious minds that come from a broad range of backgrounds, perspectives, and life experiences. We celebrate all dimensions of diversity and believe that it drives excellence and innovation, strengthening our ability to lead in science and technology. We are committed to creating access and opportunities for all to develop and grow at your own pace. Join us in building a culture of inclusion and belonging that impacts millions and empowers everyone to work their magic and champion human progress!
 
Apply now and become a part of our diverse team!