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PhD position in translational tumor immunology and bioinformatics/data science (m/w/d)

Pathologie
25.025 Stunden
auf 12 Monate befristet
ab 01.05.2024
Eingruppierung gemäß TV-L
Bewerbungsfrist 15.03.2024
Universitätsklinikum Erlangen
Pathologisches Institut
PD Dr. med. Markus Eckstein
Krankenhausstraße 8/10
91054 Erlangen
Ansprechperson bei Fragen
PD Dr. med. Markus Eckstein
Telefon: 09131 85 47792

PhD position in translational tumor immunology and bioinformatics/data science (m/w/d)
Veröffentlicht seit: 30.01.2024
Job-Nr.: 7477
PD Dr. med. Markus Eckstein
Telefon: 09131 85 47792

Universitätsklinikum Erlangen
Pathologisches Institut
PD Dr. med. Markus Eckstein
Krankenhausstraße 8/10
91054 Erlangen
PhD position in translational tumor immunology and bioinformatics/data science (m/w/d)
Veröffentlicht seit: 30.01.2024
Job-Nr.: 7477
Universitätsklinikum Erlangen
Pathologisches Institut
PD Dr. med. Markus Eckstein
Krankenhausstraße 8/10
91054 Erlangen
Ansprechperson bei Fragen
PD Dr. med. Markus Eckstein
Telefon: 09131 85 47792

Klingt spannend?
Das sind wir:

Group of translational tumor immunology, Institute of Pathology.
The group is headed by PD Dr. Markus Eckstein, M.D. and Dr. Dr. Christian Matek, and the PhD student position will be part of a project funded by the Else Kröner-Fresenius-Stiftung (PI Dr. Markus Eckstein). PhD students will have the possibility of joining the Life@FAU Graduate school for systematic training and networking to complement their project work. The group is affiliated with several important local and national institutions such as the IZKF of the FAU Erlangen-Nürnberg, the Bavarian Cancer Research Center and the National Center for Tumor Diseases (NCT).
Short Project description: Muscle-invasive (MIBC) and metastatic urothelial cancer (mUC) are an aggressive type of cancer for which overall survival rates have shown little improvement over the past few decades despite promising therapeutic advances such as immunotherapy. We previously discovered spatially organized tumor immune microenvironment phenotypes in MIBC and mUC that associate with marked survival differences and resistance to immune checkpoint inhibitor immunotherapy. However, both MIBC and mUC, despite being considered highly immunogenic tumors due to high mutational burden, have shown to be immunotherapy resistant tumors, and patients mostly progress rapidly. Underlying mechanisms of this marked immunotherapy resistance, as well as tumor evolution and metastatic progression are poorly understood on the global and functional level. We aim to address three main hypotheses:
i) Key immune evasion mechanisms are acquired and evolved during tumorigenesis, and
ii) Early committed and later acquired immune evasion mechanisms of MIBC orchestrate organotropic metastatic evolution, translating into immunotherapy resistance, but
iii) Immune evasion mechanisms driving MIBC metastatic evolution, metastatic niche colonization and immunotherapy resistance can be targeted therapeutically.
Main methods: Spatial tissue analysis including AI applications, transcriptome and exome sequencing, spatial (single) cell analysis relying on 10X platforms, bioinformatical data analysis, computer science.
The Group (clinician scientist group):
We are currently two clinician scientists (Eckstein, Matek), two postdocs, 2 PhD students, and 2 technicians plus multiple MD, DMD and Master students. The lab is international (main language: English) and is working on interdisciplinary cancer biology, tumor immunology and developmental biology of urothelial cancer using molecular and cell biology techniques, high throughput genetic analysis tools and bioinformatical data mining mainly using human patient tissues. Scientifically, we are focusing on deep spatial tumor environment analyses employing digital pathology, artificial intelligence as well as laboratory and bioinformatical workflows for single cell analytics, single cell spatial transcriptomics (Xenium platform, 10X Genomics) and spatial bulk transcriptome sequencing (Visium Spatial, 10X Genomics) including high performance computing resources for large scale data analysis and artificial intelligence applications. The group is well connected and closely collaborating with other translational researchers focusing on (tumor) microenvironment biology and single-cell spatial analyses across Germany within the framework of the BRIDGE consortium e.V., the National Center for Tumor Diseases (NCT) and the Bavarian Cancer Research Center (BZKF).

If interested in joining a multidisciplinary and internationally minded team, do not hesitate to contact us!

Your tasks:

Your main tasks pursuing the route to your PhD within the project will include:
- Scientific development of the funded project described above
- Sample and data processing for sequencing or proteogenomic approaches relying on bulk, single cell or spatial techniques.
- Establishment and maintenance of project relevant bioinformatical pipelines for processing and analyzing spatial single cell data of human cancer tissues (e.g., SpaceRanger, SquidPy, Seurat, Giotto, etc.) mainly relying on our high-performance in-house GPU cluster infrastructure and the R and python programming languages.
In-depth, hypothesis-driven bioinformatics analysis using cutting-edge data science tools.

Essential experience/qualifications:

- We are looking for interested, dynamic and agile medical scientists (MSc or MDs), who are able to develop, pursue and expand scientific projects in a cancer research area independently under close supervision.
- As your main responsibilities will be focused on data science and computational pathology, a strong interest in bioinformatics and computing, as well as pre-existing skills in coding and data analytics are recommended. Solid programming skills in R and python are considered an asset.
- We are not explicitly looking for people from specific disciplines, but for motivated applicants who intend to start a scientific career in translational cancer research with a focus on spatial tissue biology and data science.
- Adequate English speaking and writing skills are required.

Zusätzlich von Vorteil

- Advanced biological data analysis skills using R and python programming environments.
- Skills in digital pathology analysis tools such as QuPath or Cellpose
Knowledge of high-performance computing environments using current deep learning platforms such as Tensorflow and PyTorch.

We offer:

  • Work on a cutting-edge scientific project with high international visibility
  • An interesting position in a motivated open-minded international and multidisciplinary team
  • Regular team building events and activities outside the institution
  • Careful and qualified training and support for the new challenge
  • Varied and responsible field of activity with individual development opportunities
  • Comprehensive health promotion offers
  • All public service benefits, including supplementary benefits from the Federal and State Government Employees' Retirement Fund (VBL)
  • Family-friendly environment and, where appropriate, places in halls of residence
  • Crediting of previous work experience possible

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