Postdoctoral Researcher in Graph-Based Machine Learning

vor 4 Wochen

Erlangen, Bayern, Deutschland Friedrich-Alexander-Universität Erlangen-Nürnberg Vollzeit
Job Opportunity

We are seeking a highly motivated and skilled researcher to join our team at the Friedrich-Alexander-Universität Erlangen-Nürnberg. The successful candidate will work on a highly interdisciplinary project at the intersection of graph-based machine learning, precision oncology, and federated learning.

Key Responsibilities
  • Design, implement, and validate graph-based machine learning models for diagnosis and prognosis in pediatric oncology.
  • Develop federated counterparts of the machine learning models to enable privacy-preserving model training on decentrally available data.
  • Collaborate with bioinformaticians, clinical oncologists, and industrial software developers to advance the project.
Research Environment

The BIONETS Lab is part of the new Department Artificial Intelligence in Biomedical Engineering at FAU, which focuses on the development of algorithms and ML models for systems biology. The UKER's Department of Pediatrics and Adolescent Medicine has strong expertise in the large-scale quantitative analysis of multi-centre laboratory test result data in pediatric oncology.

Requirements
  • Strong research passion, scientific curiosity, and a highly independent work style.
  • Master's degree in computer science, bioinformatics, applied mathematics, or a related field with above-average grades.
  • PhD in one of these fields and an excellent publication record for postdoctoral researcher position.
  • Proficiency in both written and spoken English and strong programming skills.
Desirable Qualifications
  • Knowledge or experience in machine learning, graph-based algorithms, federated learning, or biomedical data science.
Additional Information

Funding is available through TV-L E13 positions (100%) with a duration of 2 years. Extension may be possible, depending on availability of funding.

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