PhD Student for Machine Learning in Oncology
Reference number: 2025-0183
- Frankfurt
- Full-time
- DKTK partner site Frankfurt/Mainz - Machine Learning/Bioinformatics 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
The Buettner lab (https://mlo-lab.github.io) works on the intersection of machine learning and oncology. This position is part of the prestigious ERC Consolidator Grant "TAIPO - Trustworthy AI in Personalized Oncology". You will focus on developing robust and reliable models for therapy decisions and outcomes, with an extended focus on causal inference methods.
Your Tasks
Your research will include:
- Developing causal machine learning methods for reliable survival modeling in cancer patients, particularly for AML (Acute Myeloid Leukemia)
- Building trustworthy recommender systems for therapy decisions based on electronic health records (EHR), incorporating causal reasoning and uncertainty quantification
- Creating uncertainty-aware models that can reliably communicate when predictions may be unreliable
- Collaborating with experimental and clinical partners from Heidelberg, and the DKTK network
Key research areas:
- Trustworthy survival analysis and time-to-event modeling
- Causal inference from observational health data
- Uncertainty quantification in causal models
- Integration of multi-modal data (genomics, proteomics, EHR) for time-to-event modeling
Your Profile
We are looking for a candidate with a background in computer science, statistics, bioinformatics or a related field (e.g. master’s degree in mathematics, physics, computer/data science, computational biology or a related field).
An excellent knowledge of machine learning methods and statistics is essential, as is an interest in biomedical applications and cancer research; familiarity with probabilistic modeling and uncertainty quantification is highly desirable. Very good knowledge of Python-based deep learning frameworks (PyTorch and/or TensorFlow) and best practices in software development as well as experience with Linux environments are required.
Experience with bioinformatics algorithms and biomedical AI applications is a plus.
The candidate will closely interact with other researchers and clinicians, therefore good English communication skills are also required.
To apply, please submit a single PDF file containing a cover letter, curriculum vitae, copies of relevant degree certificates with transcripts of records and contact details for at least two references.
We Offer
Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level
Access to international research networks
Doctoral salary with the usual social benefits
30 days of vacation per year
Flexible working hours
Possibility of mobile work and part-time work
Family-friendly working environment
Sustainable travel to work: subsidized Germany job ticket
Unleash your full potential: targeted training and mentoring through the DKFZ International PhD Program and DKFZ Career Service
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
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.
