Postdoc or PhD Candidate (f/m/x) - AI Augmented Target Discovery in Translational Kidney Disease
Postdoc or PhD Candidate (f/m/x) - AI Augmented Target Discovery in Translational Kidney Disease
102953
Full time
39 hrs./week
Neuherberg near Munich
Partial Home Office possible
At Helmholtz Munich, we develop groundbreaking solutions for a healthier society in a rapidly changing world. We believe that diverse perspectives drive innovation. Through strong partnerships, we accelerate the transfer of new ideas from the lab to real-life applications, improving lives.
Helmholtz Munich’s Computational Health Center and the Institute of Computational Biology (ICB) are internationally recognized for developing cutting-edge artificial intelligence (AI) and machine learning methods for biomedical research. Embedded in a highly international and interdisciplinary environment, we aim to translate complex patient and molecular data into personalized medical solutions for environmentally triggered diseases.
The Theis Lab, renowned for pioneering machine learning approaches in single-cell genomics, is seeking two highly motivated PhD candidates and/or Postdoctoral Researchers (f/m/x) in Machine Learning or Computational Biology to join this collaborative translational research project at the interface of data science, biology, and medicine.
This project is part of an academic-industry collaboration that aims to advance our understanding of kidney disease mechanisms. In this project, we are using active learning for chemical and compound perturbation selection in an iterative closed loop to generate a high quality large scale perturbation atlas — using single-cell RNA sequencing and complementary modalities such as imaging - in human kidney organoid disease models. We will train and generate multimodal artificial intelligence (AI) models on this unique resource to identify perturbations that shift kidney disease phenotypes toward healthy states and support the design of next-generation organoid models. This will guide therapeutic target discovery by predicting and experimentally testing interventions that restore cellular health.
Your tasks
- Build and analyze a translational kidney disease atlas by integrating single-cell data across species and diseases.
- Implement a framework to assess organoid disease model translatability to human patient data.
- Design large scale perturbation screening strategies, combining cost-effective technologies with single-cell RNA-seq and imaging data.
- Generate a kidney disease perturbation atlas and train AI models to develop world-leading approaches for in silico perturbation prediction, while benchmarking existing models.
- Contribute to the integration of computational predictions with wet-lab validation to ensure translational impact of the findings.
Your profile
- Currently pursuing or recently obtained a PhD, Master’s degree, or equivalent in Computer Science, Bioinformatics, Computational Biology, Biology, or a related field.
- Strong programming skills in data science workflows (e.g. Python, GitHub) and motivation to learn and apply modern deep learning frameworks (e.g. PyTorch).
- Solid understanding of molecular biology and a strong interest in complex disease models; experience with experimental techniques is welcome but not required.
- High scientific curiosity and motivation to contribute to AI-augmented disease atlases and perturbation models, combined with excellent communication skills and the ability to collaborate in interdisciplinary teams.
- Strong quantitative and mathematical reasoning skills, including familiarity with linear algebra, probability/statistics, optimization, or machine learning fundamentals, and the ability to apply these concepts to biological data analysis and model development.
Desirable qualifications:
- Experience in identifying, curating, and integrating large-scale, multimodal biological or clinical datasets.
- Familiarity with single-cell data analysis, foundation models, multi-modal integration, perturbation experiments, organoid models, or translational target discovery.
- Exposure to active learning, iterative screening, perturbation data, or target identification steps in drug development.
This position provides the opportunity for you to build specialist knowledge whilst developing key professional experience, both of which will help you to make strides in your scientific career.
Remuneration and social security benefits are based on the German Collective Wage Agreement for Public-Sector Employees at Federal Level (depending on qualifications, as PhD EG 13 50% or as Postdoc EG 13 TV EntgO Bund). In addition, there is also the possibility of receiving an allowance if the applicable conditions are met. The position has an (initial) fixed term of 3 years but may be extended under certain circumstances.
Managing Director Mike Frieser:
„We believe that diversity is the key to groundbreaking research and innovative solutions. Our flexible working models and remote options create a supportive environment for balancing work and family life. For us, contributing to a healthier society in such an inspiring workplace is truly meaningful.“
Benefits
- Scientific Training Helmholtz Munich Graduate School
- Career counseling Career Center for PhD candidates
- International Staff Service Support with the relocation and integration process in Germany
- Health Promotion Sports, company doctor, mental health initiatives
- Scientific Resources State-of-the-art infrastructure and Core Facilities
- Recreation 30 days annual leave, flexi days, plus public holidays
Interested in applying?
If you have any questions, feel free to contact Sandra Schulze, sandra.schulze@helmholtz-munich.de, who will be happy to help.