Position: PhD Student for Machine Learning in Oncology

Department: DKTK Frankfurt - Bioinformatics in Oncology

Code number: 2021-0200

The German Cancer Research Center is the largest biomedical research institution in Germany. With more than 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 Frankfurt / Mainz the German Cancer Research Center is seeking a PhD student. 

 

Job description:

We are looking for a PhD student working at the intersection of machine learning, genomics and oncoloy. Your PhD research will explore how machine learning solutions can be used to disentangle sources of variation between samples in large heterogeneous genomics data.

In particular, you will work with probabilistic latent variable models such as variational autoencoders and Gaussian Process Latent Variable models - statistical tools to infer an unobserved, hidden state of a complex (e.g. biological) system based on observable data that is often high-dimensional. In close collaboration with domain experts (clinicians and biologists), you will conceptualize, implement and apply these models to molecular profiling data (single-cell RNA-seq, proteomics, multi-omics) of cancer patients.

You will join the MLO Lab (“Machine Learning in Oncology” – https://mlo-lab.github.io) which is located on the campus of the University Hospital Frankfurt and contribute with your research to the application-driven development of interpretable and statistically sound machine learning methods for understanding disease heterogeneity.

Being at the intersection of machine learning and healthcare, this research has the potential to make significant positive impact by accelerating progress in personalised oncology.

 

Requirements:

We are looking for a candidate with a background in computer science, statistics, bioinformatics or a related field (M.Sc. is required). A good knowledge of machine learning methods and statistics is essential, as is an interest in biomedical applications and cancer research. Good knowledge of programming / scripting languages (e.g. R or Python, Python and R is a plus) and best practices in software development as well as experience with Linux environments are required.

Experience with bioinformatics algorithms and 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 send a single PDF file containing the cover letter, curriculum vitae, copies of relevant degree certificates and contact details for at least two references.

We offer:

  • Interesting, versatile workplace
  • International, attractive working environment
  • Campus with modern state-of-the-art infrastructure
  • Access to international research networks
  • Doctoral student payment including social benefits
  • Flexible working hours
  • Comprehensive training and mentoring program through the Helmholtz International Graduate School

Earliest Possible Start Date: as soon as possible

Duration: The position is limited to 3 years.

Application Deadline: 01.08.2021

Contact:

Prof. Dr. Florian Buettner
Phone +49 173 4613687

Please note that we do not accept applications submitted via email.


The DKFZ is committed to increase the proportion of women in all areas and positions in which women are underrepresented. Qualified female applicants are therefore particularly encouraged 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 (https://www.dkfz.de/en/stellenangebote/index.php).

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.