Postdoc in Computational Genomics & Single-Cell ML

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

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