Postdoc in Machine Learning & Non-invasive Tumor Profiling

Reference number: 2024-0299

  • Heidelberg
  • Full-time
  • Clinical Cooperation Unit “Multiparametric Methods for Early Detection of Prostate Cancer” and Division „Cancer Genome Research”

“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.


The research departments of Prof. Dr. Holger Sültmann (Cancer Genome Research) and PD Dr. Magdalena Görtz (Multiparametric Methods for Early Detection of Prostate Cancer) have joined forces to advance novel multi-omics profiling technologies using liquid biopsy samples to improve treatment decisions in solid tumors. Our aim is to develop diagnostic panels for non-invasive diagnosis and monitoring of tumor diseases.

The two department are jointly seeking from January 2025 a

 

Your Tasks

We are looking for an excellent bioinformatician / computational scientist to lead our efforts in multimodal tumor profiling. The core of this role is the development of integrative analysis approaches to establish disease-relevant signatures using NGS data from liquid biomaterials. You will be responsible for applying these methods to large datasets and combining data from liquid biopsies, clinical information, and imaging. Your tasks will also include the processing as well as the integration of these modalities into harmonized data structures.

You will be jointly affiliated with the laboratories of Prof. Sültmann and PD Görtz and will collaborate with partners at the DKFZ, the University Hospital of Heidelberg, as well as national and international collaborators.

Your Profile

  • Master's degree and PhD in computational biology, bioinformatics, mathematics, physics, computer science or a related discipline
  • Previous experience in NGS data analysis and management
  • Experience in big data analysis, machine learning, and modelling is desirable
  • High motivation to work in an interdisciplinary team including clinicians, wet lab scientists, and others
  • Ability to work independently and creatively
  • Excellent communication skills in English and German

We Offer

  Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level

  Remuneration according to TV-L incl. occupational pension plan and capital-forming payments

  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

  Our Corporate Health Management Program offers a holistic approach to your well-being

  Develop your full potential: access to the DKFZ International Postdoc Program and DKFZ Career Service with targeted offers for your personal development to further develop your talents

Are you interested?

Then become part of the DKFZ and join us in contributing to a life without cancer!

Contact:

Prof. Dr. Holger Sültmann
Phone: +49 (0)6221/56-5934

Duration: The position is initially limited to 2 years with the possibility of prolongation.
Application Deadline: 23.10.2024
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.

Notice: We are subject to the regulations of the Infection Protection Act (IfSG). Therefore, all our employees must provide proof of immunity against measles.

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