Try your professional wings at UMC
Expand your professional experience as a student or intern at UMC and support our work to advance the science and practice of global medicines safety.
Joining us as a student working on your master's thesis or for an internship is an opportunity to develop your skills in a challenging scientific field of social importance. For those with the right talent, it could be the first stage of a longer relationship with UMC.
UMC’s research-focused teams working in data science and pharmacovigilance science welcome a few master's thesis students annually. Other departments at UMC also sometimes engage master's students or interns.
To register your interest in working with us, please send us a brief introduction of yourself and of your area of interest, a project outline if you have something specific in mind, and your CV.
Please contact us via this page.
Master’s students turned employees share their stories
"I really liked doing my thesis at UMC. Even if I was working independently on my project, with support from my supervisor and colleagues, I got to be a part of a team from day one."
Vanja, master's student turned systems developer at UMC
"There's a lot of information and data within healthcare that can be used to help patients. I saw that UMC was using data science and machine learning to help patients within pharmacovigilance, which truly aligns with my belief that data can have a real-world impact. "
Shachi, master's student turned data scientist at UMC
"I came across UMC and I thought that the work they do is really important and very interesting. So I thought, here I can put my skills to use for good rather than for profit."
Eva-Lisa, master's student turned data scientist at UMC
Past theses undertaken at UMC
2021 Creation of a Next-Generation Standardized Drug Grouping for QT Prolonging Reactions using Machine Learning Techniques
2020 Improving the speed and quality of an Adverse Event cluster analysis with Stepwise Expectation Maximization and Community Detection
2020 Retrospective disproportionality analysis to investigate the usefulness of the WHODrug Standardised Drug Groupings in the pharmacovigilance signal detection process
2019 Extracting Adverse Drug Reactions from Product Labels using Deep Learning and Natural Language processing
2018 Deep Neural Networks for Inverse De-Identification of Medical Case Narratives in Reports of Suspected Adverse Drug Reactions
2016 Extraction of Severity Information from Clinical Narratives Using Statistical Natural Language Processing