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Max Planck Institute of Biochemistry

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Postdoctoral Research Fellow in Graph Learning Max Planck Institute of Biochemistry in Germany

Degree Level

Postdoc

Field of study

Computer Science

Funding

Full funding available

Deadline

Sep 30, 2026

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Country

Germany

University

Max Planck Institute of Biochemistry

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Keywords

Computer Science
Biomedical Engineering
Deep Learning
Biology
Statistics
Bioinformatic
Machine learning

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About this position

Postdoctoral Research Fellow (m/f/d) in Graph Learning at the Max Planck Institute of Biochemistry in Martinsried near Munich, Germany.

This postdoctoral opening is hosted by the Department of Machine Learning and Systems Biology headed by Prof. Dr. Karsten Borgwardt. The project focuses on graph learning, machine learning, graph-structured data, and geometric deep learning, with applications to frontier problems in biology using large-scale experimental datasets.

The successful candidate will develop new algorithms for graph-structured data, revisit classical graph problems with modern machine learning, and contribute to generative models for graphs. The work sits at the intersection of computer science, bioinformatics, and biology.

Eligibility: applicants should hold a Ph.D. in computer science, machine learning, bioinformatics, or a related field. Prior experience in machine learning method development is expected, ideally with a background in graph-based or geometric deep learning. Strong written and oral English skills are required.

Funding: this is a paid postdoctoral position; salary is according to qualifications and the German public service tariff scale (TVöD). The initial appointment is for two years, with the possibility of extension.

How to apply: submit a one-page letter with a personal statement, CV, bibliography, and contact details for at least two references via the application portal. Informal inquiries can be sent to borgwardt-office@biochem.mpg.de.

Deadline: 2026-09-30. Interviews are planned for October and November 2026, and the preferred start date is between December 2026 and June 2027.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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