T2604 - Research Fellow - AI-Enhanced Pharmacometrics (Intern)

At Debiopharm, every step we take is guided by one purpose: improving the lives of people affected by cancer and infectious diseases. As a privately-owned Swiss biopharmaceutical company, we’re driven by science, but above all, by the people behind every treatment — the patients and their families. 

Through our unique “development only” model, we bring forward promising therapies and transform them into treatments that can reach those who need them most, faster. 

 

Are you ready to be at the forefront of the model-informed drug development (MIDD)? Debiopharm International SA is seeking a highly motivated Research Fellow (Intern) to help build an AI-enhanced Quantitative Systems Pharmacology (QSP) platform. This project offers a unique opportunity to integrate preclinical, clinical data and real-world data with mathematical models and AI to accelerate the development of our Antibody-Drug Conjugate (ADC) pipeline. You will work directly on assets, bridging the gap between systems biology and data-driven decision-making. 

 

 

Research Fellow – AI-Enhanced Pharmacometrics (Intern)

Location: Lausanne, Switzerland 

Department: Clinical Pharmacology & Pharmacometrics 

Project: 12-months internship 

 

Executive Summary of the Internship 

The primary mission of this fellowship is to develop and standardize a custom QSP platform for the Debiopharm ADC pipeline. By utilizing AI and advanced modeling techniques, you will contribute to a "Digital Twin" environment to simulate virtual trials, optimize dosing regimens, and establish "Go/No-Go" criteria. This role is central to a specific project strategy, aiming to shorten development timelines and enhance our proprietary MIDD capabilities. 

Key Responsibilities 

• QSP Data Integration: Contribute to integrating multi data source into the existing QSP platform for key ADC programs. 

• AI-Driven Modelling: Create templates for virtual population construction using AI and R to characterize populations of interest. 

• Simulator Development: Build virtual trial simulators using R or C++ for predicting compound efficacy and safety. 

• Decision Analysis: Develop R-based data analysis templates to define "Go/No-Go" criteria based on virtual trial simulations. 

• Documentation: Thoroughly document the development process and analysis workflows to ensure knowledge retention. 

Profile Required 

• Educational Background: PhD or Master’s student in Pharmacometrics, Biostatistics, Computational Biology, Mathematics, Computer Science, or Engineering. 

• Core Knowledge: Understanding basic Pharmacokinetic (PK) and Pharmacodynamic (PD) concepts. 

• Modeling Software: Strong proficiency in R or Python is essential. Previous exposure to Monolix, NONMEM, Matlab or SimBiology is a significant advantage. Proficiency in C++ is considered a strong plus for simulator development.  

• Team Synergy: Ability to work independently on technical tasks while collaborating effectively in a team environment. 

• Organization: Strong organizational skills to manage and process data from multiple ADC programs simultaneously. 

• Communication: Fluency in English (both oral and written). 

What we Offer: 

• Being part of a company where innovation, collaboration, and impact aren’t just values — they’re how we work every day 

• Partner with teams across disciplines, at the forefront of oncology and anti-infective development 

• An inclusive and respectful workplace — proud to be Equal-Pay certified 

• Grow in a culture that values people, purpose, and performance 

• A chance to grow, share, and shape the future of healthcare 

 

What to Expect in the Recruitment Process: 

If your application is selected, you’ll be invited to interviews with Talent Acquisition and the Hiring Manager via Teams, followed by a panel interview and reference checks. Communication is handled via Workable—check your spam folder for emails from *@outbound.workable.com. 

 

Please contact our HR Department prior to submitting any profiles. We cannot accept unsolicited applications from agencies or recruiters.