“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
PhD Student for Machine Learning in Oncology
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.
Reference number: 2025-0183
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
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.
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
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