MSc Student (Thesis: From Lab to Industry - Active Learning for Chemical Data / max. 83 hours per mo

"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 DKTK Partner Site Frankfurt we are looking for as soon as possible a

We are seeking an exceptional MSc student to join our research group for "Machine Learning in Oncology" at the German Cancer Research Center (DKFZ) and Goethe University Frankfurt for an MSc thesis in collaboration with Bayer AG. The Buettner lab ( https://mlo-lab.github.io ) works on the intersection of machine learning and oncology, publishing original research in both areas at top-tier conferences and journals (e.g. Nature, NeurIPS, or ICML).

Your Tasks:

As a team member of the Buettner lab ( https://mlo-lab.github.io ), you'll have the opportunity to research at the intersection of machine learning and chemistry. Through our partnership with Bayer AG, you'll tackle real-world challenges using state-of-the-art machine learning approaches. This hands-on industry experience is powered by our lab's theoretical work in uncertainty quantification. Building upon our established methodologies in uncertainty-aware predictive analysis, you'll have the opportunity to drive innovation in this exciting field.

Join us for an exciting collaborative project investigating how different types of model uncertainty influence active learning in chemical applications. Your MSc thesis will focus on building and applying an active learning framework explicitly leveraging decompositions of model uncertainty into aleatoric and epistemic components. Working closely with Bayer AG, you'll see your theoretical advances translated into practical solutions for real chemical data challenges.

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.

To apply please submit a single PDF file containing the cover letter, curriculum vitae and transcripts of records.

We Offer

1. Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level
2. Access to international research networks
3. Flexible working hours
4. Possibility of mobile work and part-time work
5. Unleash your full potential: targeted offers for your personal development to further develop your talents
6. 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!

Contact:
Tobias Emmerich
Telefon: +49 (0)6221/42-1653

Duration: The position is limited to 6 months.

Application Deadline: 06.05.2025
Applications by e-mail cannot be accepted.

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.

Notice: We are subject to the regulations of the Infection Protection Act (IfSG). Therefore, all our employees must provide proof of immunity against measles.