Department of Biosystems Science and Engineering
The Laboratory for Biological Engineering, led by Prof. Randall J. Platt at ETH Zurich in Basel, Switzerland, is at the forefront of developing genome engineering technologies, applying them to diverse fundamental and disease-focused areas. To further our innovative efforts, we are recruiting a full-time (100%) experienced researcher to develop and implement computational methods for novel experimental functional genomics datasets.
Position Overview
Our lab builds high-throughput experimental platforms that necessitate equally innovative computational methodologies. The successful candidate will contribute significantly to two key areas:
In vivo Single-cell CRISPR Perturbation Screens
CRISPR perturbation screens, such as Perturb-seq, are revolutionizing gene function studies at scale. We have recently pioneered an AAV-based method for direct in vivo single-cell CRISPR screening and are expanding our efforts to create in vivo cell-type perturbation atlases, interrogate disease mechanisms, and identify therapeutic targets. These tasks will yield extensive, richly detailed in vivo perturbation datasets, necessitating scalable and reproducible pipelines for guide demultiplexing, cell-type annotation, and downstream perturbation-level effect estimation.
Transcriptome Recording and Cellular History Reconstruction
We are advancing our CRISPR-based transcriptional recording method, which encodes transient cellular events into DNA to be read out by sequencing. The computational challenges we face include detecting biological signals while applying Record-seq in complex in vivo environments, especially concerning drug-host microbiome interactions. The role will also involve developing specific tools and analytic workflows for the novel data produced by Record-seq and related molecular technologies.
Key Responsibilities
The candidate will primarily engage in:
- Developing analysis methods and executing experimental designs including target gene selection, power analyses, and readout selection.
- Building, maintaining, and documenting scalable, robust, and reproducible analysis pipelines (Python/R; Snakemake/Nextflow and configuration frameworks such as Hydra preferred) for cutting-edge experimental methods.
- Applying statistical methods for demultiplexing, normalization/QC, effect-size estimation, and biological inference, with a focus on guide-to-cell and cell-type assignments.
- Designing computational strategies that integrate multi-omic datasets to elucidate mechanisms in host, microbiome, and disease contexts.
- Collaborating closely with experimental biologists to apply analytical methods to ongoing projects, extracting biological insights, and aiding publication efforts.
- Contributing to biological manuscripts and computational methods papers, presenting results within the lab and at conferences, and mentoring students.
- Utilizing and maintaining lab resources on HPC and GitHub.
Qualifications
The ideal candidate will possess:
- A PhD or equivalent in Bioinformatics, Computational Biology, Computer Science, Applied Statistics, or a related field.
- Significant postdoctoral experience developing and applying computational methods to large-scale biological datasets.
- Excellent communication skills to navigate a highly interdisciplinary and international environment, with proficiency in oral and written English.
Extensive prior experience in the following areas is essential:
- Proficiency in Python and R, solid software-engineering practices, and a track record of building scalable, reproducible pipelines.
- Demonstrated experience in analyzing deep sequencing and single-cell data, including large-scale CRISPR perturbation screens.
- A strong foundation in statistics and experimental design principles.
- Experience with bioinformatics workflow design and HPC/cloud computing for running large-scale machine learning models.
- Experience in developing pipelines and workflows for novel molecular technologies and collaborating with biologists to apply these methods.
- Familiarity with multi-omic data analysis and integration.
Additional Preferred Qualifications
Prior experience in the following areas will be viewed favorably:
- CRISPR screen analysis (single-cell), library design, and analytical frameworks for in vivo CRISPR perturbation screens.
- Metagenomics, meta-transcriptomics, and metabolomics data analysis.
- Machine learning for genomics, including biologically grounded perturbation prediction models.
- Multi-omic integration and genome-scale metabolic modeling.
Application Process
This position is located in the Department of Biosystems Science and Engineering (D-BSSE) at ETH Zurich in Basel, Switzerland. D-BSSE specializes in systems and synthetic biology, bioinformatics, data science, and engineering sciences. The ETH Zurich is recognized globally as a leader in science and technology, consistently ranking among the world’s top universities.
We encourage interested candidates to apply online using the form below. Only applications matching the job profile will be considered.