Postdoc in deep-learning driven single-cell transition analysis for digital pathology (f/m/x)
Full time
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. We particularly excel in the fields of basic research, bioengineering, artificial intelligence, and technological development.
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.
This is what drives us. Why not join us and make a difference?
We are seeking a postdoc (f/m/x) to our newly established sister lab at the AI4Health Institute at Helmholtz Munich.
The candidate will lead the activities in applied machine learning. The project aims to develop novel methods to uncover the cellular transitions and complexity of tumors at a single cell level (see Hirling etal 2023 Nature Methods; Toth etal. 2022 Cell Reports; Mund etal. 2022 Nature Biotechnology; Szkalisity etal. 2021 Nature Communications).
More info about the BIOMAG group:
https://group.szbk.u-szeged.hu/sysbiol/horvath-peter-lab-index.html
"We believe that excellent research requires a range of different perspectives. Diverse teams reach better solutions and are more innovative in their research topics.
Establishing our Diversity Management Strategy demonstrates our commitment to ensuring an appreciative company culture based on mutual respect. We are also implementing diversity-sensitive processes throughout our whole organization."
Your tasks
The major focus will be on how to model cellular state transitions and how to compare visual appearance of single-cells along tissue slides using generative models and ViTs in a supervised, self or weakly supervised context.
Our lab has access to hundreds of thousands of pathology slides of different tumors. The aim is to build an intelligent large-scale system that may uncover the ways how cells undergo severe state changes, such as the development of cancer.
Your profile
- PhD in computer science, math, physics or similar fields, specialized on image analysis or machine learning
- Interest in biological applications
- Strong programming skills
- Programming in Python ML frameworks
- Team player who is also able to work independently, especially as the lab is small in the beginning
- Proficiency in both written and spoken English
Desirable qualifications:
- Experience with active learning, semi-supervised learning, or energy minimization techniques is a plus
- Strong background in mathematics and statistics is a plus
Benefits
Since 2005, we hold the TOTAL E-QUALITY award for exemplary action in the sense of an equal-opportunity organizational culture.
Helmholtz Munich is actively committed to diversity and inclusion in practice and is sustainably committed to equality.
The Diversity Charter has set itself the goal of promoting diversity in the world of work. By signing the charter, we commit ourselves to create an appreciative working environment for all employees.
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. Please request the Statement of Comparability for Foreign Higher Education Qualifications as early as possible.
