German Cancer Research Center in the Helmholtz Association (DKFZ)

PhD Student

German Cancer Research Center in the Helmholtz Association (DKFZ)

Munich, Germany

The German Cancer Research Center is the largest biomedical research institution in Germany. With approximately 3,000 employees, we operate an extensive scientific program in the field of cancer research.

Together with university partners at seven renowned partner sites, we have established the German Cancer Consortium (DKTK).

For the partner site of DKTK München the German Cancer Research Center is seeking a

PhD Student
(Ref-No. 2020-0007)

The Department of Diagnostic and Interventional Radiology at the Technical University of Munich, Klinikum Rechts der Isar location, is seeking to appoint one PhD student for the development of novel end-to-end deep-learning architectures for the analysis of medical images and their associated clinical records in pancreatic cancer patients.

The development of such algorithms will advance the fields of radiology and oncology and enable better pre-therapeutic patient stratification, improve clinical decision making and ultimately improve pancreatic cancer prognosis.

You will be tasked with the implementation of multi-input, end-to-end deep learning architectures using current state-of-the-art tools by working in a multidisciplinary team of radiologists, machine-learning researchers, oncologists and fellow students. You will be jointly supervised by the Department of Diagnostic and Interventional Radiology at TUM and the Department of Computing at Imperial College London and will have the opportunity to participate in the active academic exchange between our institutions.

You will have the opportunity to work at the forefront of biomedical imaging analysis, using large, proprietary datasets, and using contemporary techniques such as transfer learning, federated and privacy-preserving AI, decentralised systems and multimodal deep learning.

Your profile:

  • An M.Sc. (or equivalent) in an area pertinent to the subject, i. e. (biomedical) computing, mathematics or data / software engineering with a focus on machine learning
  • Proven track record (courses, extracurricular activities, portfolio, blog posts etc.) in machine learning, especially in the areas of deep learning, computer vision and biomedical imaging analysis
  • Practical experience with multidisciplinary research and supervision of students
  • Practical experience with industry standard software engineering practices (version control, test-driven development, documentation-writing, reproducible AI) is highly desired
  • Excellent Python programming skills are required
  • Experience with front-end web development and other languages such as Julia or Swift is desired
  • Excellent oral and written communication skills in English, knowledge of the German language is not a requirement
  • Ability to self-organise, address and communicate findings and issues with superiors in a time-efficient and structured manner is required
  • A publication track record is desired but not required

The application should include the following documents:

  • A one-page cover letter
  • A full CV
  • A two-page statement detailing your research interests, how your experience and expertise is relevant to the posting, and why you feel you are personally suited for the position
  • Applicants will be invited for an informal meeting and a 45 minute technical interview at a later time
    The position is limited to 3 years.

For further information please contact
Rickmer Braren, phone 089 4140-5627.

The German Cancer Research Center is committed to increase the percentage of female scientists and encourages female applicants to apply. Among candidates of equal aptitude and qualifications, a person with disabilities will be given preference.

To apply for a position please use our online application portal (">*.

We ask for your understanding that we cannot return application documents that are sent to us by post (Deutsches Krebsforschungszentrum, Personalabteilung, Im Neuenheimer Feld 280, 69120 Heidelberg) and that we do not accept applications submitted via email. We apologize for any inconvenience this may cause.

Please apply via recruiter’s website.

Quote Reference: Ref-No. 2020-0007