Postdoc (f/m/x) bei Helmholtz München

Postdoc to advance research in Medical Foundation Models (f/m/x)

102624

Full time
39 hrs./week

Neuherberg near Munich

Home Office Options

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

Our research is focused within the areas of metabolic health/diabetes, environmental health, molecular targets and therapies, cell programming and repair, bioengineering, and computational health. Through this research, we build the foundations for medical innovation. Together with our partners, we seek to accelerate the transfer of our research, so that laboratory ideas can reach society and improve people’s quality of life at the fastest rate possible.

Join us and use your talents and passion as we work together to drive forward scientific progress.

The Institute of Machine Learning in Biomedical Imaging (IML) is at the forefront of developing machine learning solutions for biomedical imaging. With a mission to tackle unmet clinical needs, the institute explores novel computational methods to improve diagnostics, prognostics, and patient care. Under the leadership of Julia Schnabel, the research at IML addresses challenges in cancer, cardiovascular diseases, and maternal/perinatal health, striving to make healthcare more accurate, accessible, and impactful.

We are seeking a highly motivated Postdoc (f/m/x) to advance research in Medical Foundation Models with a focus on biomedical imaging and healthcare applications. The position is available from 1st March 2025.

Prof. Dr. Julia Anne Schnabel, Head of Institute of Machine Learning in Biomedical Imaging (IML):

"My research focus is on developing novel machine learning solutions for medical imaging applications, such as early detection and characterisation of disease, and prediction of treatment outcome."

Your tasks

As a Postdoc (f/m/x) in Foundation Models, you will:

Push boundaries in model development: Build and adapt foundation models (e.g., large-scale generative or transformer-based architectures) for applications in biomedical imaging and clinical data integration.
Advance personalised healthcare: Explore the use of foundation models to unify and analyse multi-modal data, including imaging, genomic, and clinical datasets, for personalised diagnostics and treatment.
Optimise model efficiency: Develop innovative techniques to improve scalability, efficiency, and accuracy of foundation models for practical clinical implementation.
Lead interdisciplinary collaborations: Work closely with clinicians, biologists, and data scientists to identify key challenges and deliver impactful solutions.
Disseminate knowledge: Publish research in top-tier journals, present at international conferences, and mentor junior researchers.

What we expect from you:

Share your research through publications in renowned journals and by presenting your insights at prestigious international conferences, showcasing your work to a global audience.
Guide and mentor students and doctoral candidates, sharing your expertise to help them succeed.
Take part in teaching activities within the institute and beyond.
Proactively seek funding for your research and build collaborations to push boundaries even further.

Your profile

You hold a PhD in computer science, applied mathematics, biomedical engineering, or a related field.
You have a strong publication record in high-impact journals (e.g., Nature Machine Intelligence, Medical Image Analysis, IEEE Transactions on Medical Imaging).
You have expertise in machine learning, particularly in large-scale models (e.g., transformers, diffusion models, or generative architectures).
You are skilled in Python, C/C++ and familiar with libraries such as PyTorch or TensorFlow.
You have experience with large datasets, including pre-processing, model training, and evaluation.
You thrive in interdisciplinary and collaborative environments, bringing strong problem-solving skills and a proactive mindset.
You have excellent communication and presentation skills, with fluency in spoken and written English.

Desirable qualifications

Experience with federated learning, multi-modal data integration, or domain-specific adaptations of foundation models.
Familiarity with cloud computing and high-performance computing environments.
Initial experience in securing research funding and teaching.

Benefits

Work-Life-Balance

Flexible working hours and flexi-time models

Personal Development

Continuous education and training

Recreation

30 days annual leave, flexi days, plus public holidays

Family Support

On-site kindergarten, holiday care, care for the elderly

Health Promotion

Sports, company doctor, mental health initiatives

Retirement Provision

Company pension plan

Work-Life-Balance: Flexible working hours and flexi-time models
Personal Development: Continuous education and training
Recreation: 30 days annual leave, flexi days, plus public holidays
Family Support: child care, holiday care, care for the elderly
Health Promotion: Sports, company doctor, mental health initiatives
Retirement Provision: Company pension plan

If you fulfil all the requirements, you may be eligible for a salary grade of up to E 13. Social benefits are based on the Collective Wage Agreement for Public-Sector Employees (TVöD). The position has an (initial) fixed term of 2 years but may be extended under certain circumstances.

We are committed to promoting a culture of diversity and welcome applications from talented people regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical abilities, religion or age. Qualified applicants with physical disabilities will be given preference.
If you have obtained a university degree abroad, we will require further documents from you regarding the comparability of your degree.

Interested in applying?

If you have any questions, feel free to contact Sandra Mayer, +4989318749207, who will be happy to help.

Your application should include

CV
Degrees/Diplomas/Certificates
References from recent employers

Postdoc (f/m/x)