MSc Student (Thesis in Machine Learning for Chemical Data / max. 83 hours per month)
Reference number: 2025-0005
- Frankfurt
- Part-time
- DKTK partner site Frankfurt/Mainz - Machine Learning in Oncology
“Research for a life without cancer" is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention strategies. We develop new methods with which tumors can be diagnosed more precisely and cancer patients can be treated more successfully. Every contribution counts – whether in research, administration or infrastructure. This is what makes our daily work so meaningful and exciting.
Together with university partners at seven renowned partner sites, we have established the German Cancer Consortium (DKTK).
For the Research Group "Machine Learning in Oncology” (headed by Prof. Dr. Florian Buettner) at the DKTK partner site Frankfurt/Mainz and the Goethe University Frankfurt, we are seeking for the next possible date a motivated
The Buettner lab (https://mlo-lab.github.io) works on the intersection of machine learning and oncology and as such is actively pursuing original research in both areas. Your MSc research at the intersection of machine learning and chemistry will explore how machine learning solutions can be used for uncertainty-aware predictive analysis of chemical data.
Your Tasks
Join us for an exciting collaborative project where we will investigate the interplay of different types of model uncertainty on active learning tasks in the context of chemical data. Your MSc thesis will focus on building and applying an active learning framework explicity leveraging decompositions of model uncertainty into aleatoric and epistemic components. In collaboration with Bayer AG, you will apply these models to real-world chemical data.
Your Profile
- Current enrollment in a Master's program in computer science, statistics, applied mathematics, or related field at a German university
- A good knowledge of machine learning methods and statistics is essential; familiarity with probabilistic modeling and uncertainty quantification is highly desirable
- Very good knowledge of Python and best practices in software development as well as experience with Linux environments are required
The candidate will closely interact with other researchers, therefore good English communication skills are also required.
We Offer
Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level
Access to international research networks
Flexible working hours
Unleash your full potential: targeted offers for your personal development to further develop your talents
Our Corporate Health Management Program offers a holistic approach to your well-being
Are you interested?
Then become part of the DKFZ and join us in contributing to a life without cancer!
Prof. Dr. Florian Buettner
Phone: +49 173 4613687
We are convinced that an innovative research and working environment thrives on the diversity of its employees. Therefore, we welcome applications from talented people, regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical ability, religion and age. People with severe disabilities are given preference if they have the same aptitude.
