Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology

1000scholars; Genf; On-site; CHF 70,000 - 110,000; Full time

Job summary

EPFL in Geneva seeks a postdoctoral researcher for computational genomics, machine learning, and single-cell biology. The Zenk Lab (NeuroNA Chair in Epigenomics of Neurodevelopmental disorders – EpiGN) offers an interdisciplinary environment at EPFL with close collaboration across labs, combining experimental biology and computational genomics.

The project focuses on integrating and analyzing multi-modal single-cell datasets (scRNA-seq, scATAC-seq, scCUT&Tag, HiC, proteomics) to uncover

Qualifications

• 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.

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

Skills

Computational biology

Bioinformatics

Machine learning

Single-cell data analysis

Data integration

Python

R

Tools

Python

R

scRNA-seq analysis

Job description

Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology

EPFL - Swiss Federal Institute of Technology Lausanne

Geneva, Switzerland

Mission

The Zenk Lab (NeuroNA Chair in Epigenomics of Neurodevelopmental disorders – EpiGN) is 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 integrate multimodal data using machine learning approaches to uncover principles of cellular state transitions. The work will be carried out in close collaboration with other labs at EPFL, offering a uniquely rich environment combining experimental biology and computational genomics.

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