myScience
Lausanne
Vor 8 Stunden
Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology
- 13 Februar 2026
- 100%
- Lausanne
Über den Job
Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology
Workplace Lausanne - Lake Geneva region - Switzerland CategoryLife Sciences | Research Management
Position Senior Scientist / Postdoc
Published 12 February 2026 EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries.
Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology
The project focuses on the integration and analysis of multi-modal single-cell datasets, including scRNA-seq, scATAC-seq, scCUT&Tag, HiC, and proteomics, with an emphasis on trajectory inference, regulatory dynamics, and cell fate decisions. A key goal is to develop and apply machine learning approaches, ranging from probabilistic models to modern representation learning, to uncover principles of cellular state transitions. The work will be carried out in close collaboration with the Deplancke lab and the Brbic lab at EPFL, offering a uniquely rich environment combining experimental biology, computational genomics, and ML methodology.
Activity Rate : 100.00
Contract Type: CDD
Duration: 12 months
Reference: 2072
Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology
Mission
We are seeking a highly motivated postdoctoral researcher with strong training in computational biology and/or machine learning to join an interdisciplinary project at the interface of single-cell genomics, data integration, and developmental biology.The project focuses on the integration and analysis of multi-modal single-cell datasets, including scRNA-seq, scATAC-seq, scCUT&Tag, HiC, and proteomics, with an emphasis on trajectory inference, regulatory dynamics, and cell fate decisions. A key goal is to develop and apply machine learning approaches, ranging from probabilistic models to modern representation learning, to uncover principles of cellular state transitions. The work will be carried out in close collaboration with the Deplancke lab and the Brbic lab at EPFL, offering a uniquely rich environment combining experimental biology, computational genomics, and ML methodology.
Main duties and responsibilities
- Working and collaborating on research projects
- Analysis and publication of results
- Build a strong network in the field of research
- Participate in education, and PhD and master student supervision
Profile
- Strong background in computational biology, bioinformatics, machine learning, or a related quantitative field
- Experience with machine learning and/or statistical modeling applied to biological data
- Proven expertise in single-cell data analysis (scRNA-seq and/or scATAC-seq)
- Interest or experience in multi-modal data integration and trajectory inference
- Proficiency in Python and/or R
- Interest in developmental and human biology is highly valued, but not required
- Strong collaborative mindset and ability to work across disciplines
We offer
- A highly interdisciplinary research environment at the interface of ML and biology
- Access to large, state-of-the-art single-cell multi-omics datasets
- Close collaboration with leading groups in single-cell genomics and machine learning
- Strong support for high-impact publications and career development
Informations
Contract Start Date : 06/01/2026Activity Rate : 100.00
Contract Type: CDD
Duration: 12 months
Reference: 2072
In your application, please refer to myScience.ch and referenceJobID69250.
