Postdoc (f/m/x) in Machine Learning in Biomedicine

Helmholtz Zentrum München; Oberschleißheim; Vor Ort; EUR 50.000 - 70.000; Vollzeit

Zusammenfassung

A leading research institution in Germany is seeking a Postdoc (f/m/x) in Machine Learning in Biomedicine to develop AI tools for clinical diagnostics. Ideal candidates will hold a PhD and possess strong skills in AI/machine learning, with an interest in biomedical questions. This position offers opportunities for collaboration and research visits. Salary is based on the TVöD collective agreement with a fixed term of up to 2 years.

Qualifikationen

• Strong skills in AI/machine learning and their applications.
• Interest or background in biomedical questions.
• Excellent analytical and problem-solving skills.

Aufgaben

• Develop decentralized automated machine learning models for diagnostics.
• Create a disease severity classifier for COVID-19.
• Supervise and guide students.

Kenntnisse

AI/machine learning

Analytical skills

Problem-solving

Team collaboration

Ausbildung

PhD in computer science, computational biology, or related fields

Tools

Scanpy

Jobbeschreibung

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Job Reference:

b78759371abf

Job Views:

2

Posted:

11.08.2025

Expiry Date:

25.09.2025

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Job Description:

Postdoc (f/m/x) in Machine Learning in Biomedicine

102301

We are Helmholtz Munich. In a rapidly changing world, we discover breakthrough solutions for better health.

Our research focuses on metabolic health/diabetes, environmental health, molecular targets and therapies, cell programming and repair, bioengineering, and computational health. We excel in basic research, bioengineering, AI, and technological development.

We aim to translate our research into medical innovations to improve people's lives.

The Computational Health Center (CHC) at Helmholtz Munich and TU Munich is known for data analysis and modeling of biological systems. The Theis Lab specializes in machine learning and AI in molecular biology, especially single-cell genomics and microscopy. The position involves collaboration with Joachim Schultze in Bonn.

Prof. Matthias Tschöp emphasizes diversity and inclusion, fostering an appreciative culture.

The role involves developing decentralized machine learning/AI tools for analyzing high-parameter flow cytometry and single-cell multi-omics data for diagnostics in a clinical multi-center study, using Scanpy and Swarm Learning. It is part of a national consortium on "Swarm Learning for precision medicine in infectious diseases and pandemic preparedness".

This position offers opportunities for collaboration with the Schultze Lab at the German Center for Neurodegenerative Diseases and potential research visits to Bonn. The work aims to pioneer AI applications in medicine.

If you are passionate about AI in medicine and advancing precision medicine, consider applying.

• Develop decentralized, automated machine learning models for clinical single-cell flow and multi-omics data diagnostics, utilizing deep representation learning and attention-based multiple instance learning.
• Interpret models via attention mechanisms and prior knowledge.
• Create a disease severity classifier for COVID-19 based on high-parameter cytometry data and integrate it with multimodal single-cell omics data.
• Supervise and guide students.

Your profile

• PhD in computer science, computational biology, or related fields.
• Strong skills in AI/machine learning and their applications.
• Interest or background in biomedical questions.
• Excellent analytical and problem-solving skills.
• Team player with good communication and interdisciplinary collaboration skills.

Helmholtz Munich values diversity and equality, holding the TOTAL E-QUALITY award since 2005 and actively promoting an inclusive culture.

The position offers a salary up to E 13, based on the TVöD collective agreement, with a fixed term of 2 years, possibly extendable.

We encourage applications from all talented individuals regardless of gender, background, ethnicity, or abilities. Applicants with disabilities are given preference. If your degree was obtained abroad, additional documentation regarding its comparability is required.