Experienced Postdoc Researcher in Development and application of computational methods for functional genomics

Position Overview

Join a dynamic and innovative research team at the Laboratory for Biological Engineering at ETH Zurich in Basel, Switzerland. This collaborative environment focuses on developing genome engineering technologies for both fundamental research and disease applications. The team is engaged in cutting-edge projects aimed at advancing experimental functional genomics through the creation and application of novel computational methods.

Key Responsibilities

• Develop analysis methods and execute experimental design, focusing on target gene selection, power analyses, and readout selection.
• Build, maintain, and document scalable and reproducible analysis pipelines using Python and R, with preferences for Snakemake/Nextflow and Hydra.
• Apply statistical methods for demultiplexing, normalization/QC, and effect-size estimation, concentrating on guide-to-cell assignment and model development using machine learning.
• Design computational strategies for integrating multi-omic datasets, elucidating biological mechanisms in various contexts.
• Collaborate with experimental biologists to leverage analytical methods for ongoing projects and contribute to technological and biological insights.
• Participate in writing biological manuscripts and computational papers and present findings at conferences.
• Utilize lab resources on HPC and Github as part of daily activities.

Qualifications

The ideal candidate will possess:

• A PhD or equivalent in Bioinformatics, Computational Biology, Computer Science, Applied Statistics, or a related field.
• Substantial postdoctoral or similar experience in developing computational methods for large-scale biological datasets.
• Exceptional communication skills for a collaborative, interdisciplinary, and international environment with proficiency in English.

Technical Expertise

Extensive prior experience in the following areas is essential:

• Strong skills in Python and R, proficient in building scalable, reproducible data pipelines.
• Analysis of deep sequencing and single-cell data, including multi-omics datasets.
• Strong foundation in statistics and experimental design principles, especially in perturbation effect estimation.
• Experience with bioinformatics workflow design and HPC/cloud computing, including deep learning model implementation.

Additional Experience

Prior experience in the following areas will be considered an advantage:

• CRISPR screen analysis, including robust guide-to-cell assignment frameworks.
• Familiarity with metagenomics and metabolomics data analysis, particularly in gut microbiome studies.
• Machine learning applications in genomics and methods for multi-omics integration.
• Genome-scale metabolic modeling related to microbial communities.

Location and Environment

This position is based in the Department of Biosystems Science and Engineering at ETH Zurich. The D-BSSE is interdisciplinary, specializing in systems and synthetic biology, bioinformatics and data science, and engineering sciences. Located in Basel, the department is part of a vibrant biomedical research hub with connections to leading academic institutions and major biotech companies.

Basel is an international city offering access to cultural activities, nature, and connectivity throughout Europe, providing a stimulating environment for both work and leisure.