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PhD Position in Computer Science and Biomedical Data Science

vor 2 Monaten


Erlangen, Bayern, Deutschland Friedrich-Alexander-Universität Erlangen-Nürnberg Vollzeit
About the Position

The Biomedical Network Science Lab at the Friedrich-Alexander-Universität Erlangen-Nürnberg and the Department of Pediatrics and Adolescent Medicine at the University Hospital Erlangen are seeking a highly motivated researcher to join their team. 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 using real-world data from actual patients.
  • 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 was established as a cornerstone of the health node of the Bavarian High-Tech Agenda. Research at the BIONETS Lab focuses on the development of algorithms and ML models for systems biology, while 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.
  • For PhD position: Master's degree in computer science, bioinformatics, applied mathematics, or a related field with above-average grades.
  • For postdoctoral researcher position: PhD in one of these fields and an excellent publication record.
  • Proficiency in both written and spoken English and strong programming skills.
Desirable Qualifications
  • Knowledge or experience in one or several of the following fields: machine learning / deep learning, graph-based algorithms and machine learning models, federated learning, biomedical data science.
Terms and Conditions

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