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PhD Student for Machine Learning in Oncology

German Cancer Research Center (DKFZ) - DKTK Frankfurt - Bioinformatics in Oncology

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 Frankfurt / Mainz the German Cancer Research Center is seeking a

PhD Student for Machine Learning in Oncology (Ref-No. 2021-0200)
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.

Your profile:

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

The position is limited to 3 years.

For further information please contact Prof. Dr. Florian Buettner, phone +49 173 4613687.

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 (www.dkfz.de).

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.

Qualification Type: PhD
Location: Frankfurt - Germany
Funding for: EU Students, International Students, Self-funded Students, UK Students
Funding amount: Not Specified
Hours: Full Time
Placed On: 13th July 2021
Closes: 8th August 2021
Reference: 2021-0200
   
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