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 to advance both fundamental and disease-focused research. We are seeking a full-time (100%) experienced researcher with expertise in computational methods aimed at tackling novel experimental functional genomics datasets.
About the Role
Our lab builds high-throughput experimental platforms, necessitating the development of innovative computational techniques. The successful candidate will contribute to two primary areas:
- In vivo single-cell CRISPR perturbation screens: Our team is transforming the study of gene function at scale. Having developed an AAV-based method for direct in vivo single-cell CRISPR screening, we are expanding these efforts to create in vivo cell-type perturbation atlases, investigate disease mechanisms, and identify therapeutic targets. This work will produce extensive in vivo perturbation datasets, requiring robust pipelines for guide demultiplexing, assignment, cell-type annotation, and effect estimation, alongside the development of sophisticated machine learning models.
- Transcriptome recording and cellular history reconstruction: We are advancing our CRISPR-based transcriptional recording methods that encode transient cellular events into DNA, which we decode through sequencing. Challenges include detecting biological signals in complex environments, particularly drug-host microbiome interactions, and designing tools and workflows to harness the novel data generated by this technology.
Key Responsibilities
The selected candidate will engage in the following activities:
- Develop analysis methods and execute experimental designs, including target gene selection, power analyses, guide-library design, and readout selection.
- Construct, maintain, and document scalable analysis pipelines (using Python/R, Snakemake/Nextflow, and configuration frameworks such as Hydra) for innovative experimental techniques.
- Develop and apply statistical methods for demultiplexing, normalization, effect-size estimation, biological inference, and predictive modeling.
- Design computational strategies to integrate multi-omic datasets, elucidating mechanisms in host, microbiome, and disease contexts.
- Collaborate closely with experimental biologists to apply analytical methods, extract biological insights, and contribute to publications.
- Utilize and maintain lab resources on HPC and GitHub.
Qualifications
The ideal candidate will possess:
- A PhD or equivalent degree in Bioinformatics, Computational Biology, Computer Science, Applied Statistics, or a related field.
- Substantial postdoctoral or equivalent experience in developing computational methods for large-scale biological datasets.
- Proficiency in English, both oral and written, suitable for a highly interdisciplinary international environment.
Essential Skills
Proven experience in the following areas is critical:
- Strong programming skills in Python and R, along with solid software engineering practices.
- Experience in analyzing deep sequencing and single-cell data, including large-scale in vivo Perturb-seq datasets.
- Grounding in statistical methods and experimental design principles applied to perturbation-effect estimation.
- Familiarity with bioinformatics workflow design and HPC/cloud computing.
- Previous development of analytic workflows for novel molecular technologies.
Desirable Experience
Additional experience in the following areas will be considered advantageous:
- CRISPR screen analysis and robust guide-to-cell assignment.
- Analysis of metagenomics, meta-transcriptomics, and metabolomics data.
- Machine learning applications in genomics.
- Multi-omics integration techniques.
- Genome-scale metabolic modeling.
Location and Culture
This position is based in the Department of Biosystems Science and Engineering (D-BSSE) at ETH Zurich in Basel, Switzerland. The D-BSSE is a multidisciplinary department specializing in systems and synthetic biology, bioinformatics, and data science. Located within a prominent biomedical research hub, our department collaborates closely with leading academic institutions and major biotechnology and pharmaceutical companies. ETH Zurich consistently ranks among the top universities globally, situated in Basel, a vibrant international city with convenient access to various cultural and natural attractions throughout Europe.
Application Process
Apply online using the form below. Only applications matching the job profile will be considered.