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
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
Helmholtz Zentrum München
Forschung
Oberschleißheim
- Art der Beschäftigung: Vollzeit
- 54.000 € – 68.500 € (von XING geschätzt)
- Hybrid
Postdoc or PhD Candidate (f/m/x) - AI Augmented Target Discovery in Translational Kidney Disease
Über diesen Job
Postdoc or PhD Candidate (f/m/x) - AI Augmented Target Discovery in Translational Kidney Disease
102842
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.
In close collaboration with Novartis Pharma AG, this project bridges academia and industry to advance AI-augmented target discovery for kidney diseases. The research combines integrated single-cell atlases, organoid perturbation models, and foundation models for single-cell transcriptomics to connect patient-derived data with preclinical research.
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.
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.
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 4 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."