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

Experienced Researcher in Development and application of computational methods for functional genomics
100%, Basel, fixed-term

The Laboratory for Biological Engineering (Prof. Randall J Platt) of the ETH Zurich in Basel, Switzerland develops genome engineering technologies and applies them to a range of fundamental and disease-focused areas. To advance these efforts, the Platt group is recruiting a full-time (100%) Postdoctoral Associate to develop and apply computational methods for novel experimental functional genomics datasets.

Project background

Our lab builds high-throughput experimental platforms that require the development of equally innovative computational methods. Two areas in which the candidate will contribute include:

• In vivo single-cell CRISPR perturbation screens
  
  CRISPR perturbation screens, such as Perturb-seq, are transforming how we study gene function at scale. We recently developed an AAV-based method for direct in vivo single-cell CRISPR screening (Santinha, Nature, 2023) and are expanding this to generate in vivo cell-type perturbation atlases, interrogate disease mechanisms, and identify therapeutic targets. These efforts will generate large-scale, rich in vivo perturbation datasets, requiring scalable and reproducible pipelines for guide demultiplexing and assignment, cell-type annotation using bespoke references, and downstream perturbation-level and gene-level effect estimation, as well as the development of sophisticated approaches and biologically grounded perturbation prediction models incorporating machine learning and modeling.

• Transcriptome recording and cellular history reconstruction
  
  We are advancing our CRISPR-based transcriptional recording method (Schmidt, Nature, 2018; Tanna, Nature Protocols, 2020) that encodes transient cellular events into DNA and reads them out by sequencing. Computational challenges include detection of biological signals while applying Record-seq to complex in vivo environments (Schmidt, Science, 2022), especially in the context of drug-host microbiome interactions, and the development of dedicated tools and analytic workflows for the novel data modality generated by Record-seq and related molecular technologies.

Job description

We are looking for a highly motivated and collaborative researcher to join us in advancing these efforts. The candidate will work as part of a multidisciplinary team and be passionate about science, technology, collaboration, and communication. The candidate will work closely with laboratory members (experimental and computational) and engage in planning projects and experiments, developing computational methods, analyzing as well as integrating omics datasets (including transcriptomics, metagenomics, metabolomics, proteomics, and other modalities), and interpreting findings.

The candidate will primarily be engaged in the following activities:

• Develop analysis methods and execute experimental design (target gene selection, power analyses, guide-library design, readout selection), including prioritizing targets by re-analyzing large-scale in vivo scRNA-seq datasets and multi-modal atlases.
• Build, maintain, and document scalable, robust, and reproducible analysis pipelines (Python/R; Snakemake/Nextflow and configuration frameworks such as Hydra preferred) for novel experimental methods, including in vivo Perturb-seq, transcriptional recording (Record-seq), and related technologies.
• Develop and apply statistical methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling, with a focus on confident guide-to-cell assignment, cell type assignment using custom references, perturbation-level and gene-level effect estimation, and biologically grounded perturbation prediction models using machine learning (e.g. PyTorch/JAX on HPC).
• Design and refine computational strategies that integrate multi-omic and multi-modal datasets (e.g., transcriptomics, metagenomics, metabolomics, proteomics) to elucidate mechanisms in host, microbiome, and disease contexts.
• Work closely with experimental biologists to apply these analytical methods to ongoing projects, extract compelling biological insights, and help drive biological and technology-focused publications.
• Contribute to biological manuscripts and computational methods papers, present results within the lab and at conferences, and help mentor students.
• Use and maintain lab resources on HPC and Github.

Tools you will use daily include Python and R, version control (Git), and HPC schedulers.

Profile

The ideal candidate will have:

• at least a PhD or equivalent in Bioinformatics
• Computational Biology
• Computer Science
• Applied Statistics or a related field
• a substantial postdoctoral or equivalent experience developing and applying computational methods to large-scale biological datasets

The candidate must be able to communicate effectively in a highly interdisciplinary and international environment, which includes a mastery of oral and written English.

Extensive prior experience in the following is essential:

• Strong Python and R skills; solid software-engineering practices (testing, packaging, documentation, Git); and a track record of building scalable, reproducible pipelines for large-scale in vivo perturbation and multi-omics datasets (e.g. Snakemake/Nextflow, Hydra-based configuration, containerization).
• Demonstrated experience analyzing deep sequencing and single-cell data (e.g., Scanpy/Seurat, count models, batch correction, differential analyses), including large-scale in vivo Perturb-seq or related CRISPR-based perturbation screens with robust guide demultiplexing and assignment, and cell-type annotation using bespoke references.
• Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles, especially as applied to perturbation-effect estimation at both perturbation and gene levels.
• Bioinformatics workflow design (Snakemake/Nextflow) and HPC/cloud computing, including running large-scale machine learning models (e.g., PyTorch, JAX) on GPUs in an HPC environment and maintaining reproducible workflows.
• Extensive prior experience developing pipelines and analytic workflows for novel molecular technologies (such as transcriptional recording), working closely with biologists to apply these methods to real datasets, and co-authoring biological or technology papers based on these approaches.
• Experience with multi-omic and multi-modal data analysis and integration, including combining transcriptomics, metagenomics, metabolomics, proteomics, and related modalities.

Prior experience in the following is a major plus:

• CRISPR screen analysis (single cell), guide demultiplexing, library design, robust guide-to-cell assignment, and analysis frameworks for in vivo CRISPR perturbation screens.
• Metagenomics, meta-transcriptomics and metabolomics data analysis and extensive familiarity with gut microbiome research.
• Machine learning for genomics (representation learning, generative models, causal inference), including biologically grounded perturbation prediction models implemented in frameworks such as PyTorch and JAX.
• Multi-omics integration (scRNA-seq + CRISPR barcode/perturbation; metagenomics/meta-transcriptomics/metabolomics; transcriptomics/proteomics), including joint modeling of multi-modal datasets.
• Genome-scale metabolic modeling applied to single microbes and their communities.

Workplace
Workplace
We offer

The position is located in the Department of Biosystems Science and Engineering (D-BSSE) of the ETH Zurich in Basel, Switzerland. The D-BSSE is a highly interdisciplinary department – specializing in systems and synthetic biology, bioinformatics and data science, and engineering sciences. The D-BSSE is centrally located within a biomedical research hub with close links to top academic institutions (e.g., Swiss Institute of Bioinformatics, Friedrich Miescher Institute (FMI), Biozentrum and University of Basel) as well as major biotechnology and pharmaceutical companies (e.g., Novartis, Roche, Bayer, and Lonza). The ETH Zurich is a global leader in science and technology and consistently ranks as one of the top universities in the world. Basel, Switzerland is an international city on the border with France and Germany – nested between the Swiss alps and the black forest. The city provides easy access to arts and culture, nature and adventure, and short commutes via train/plain/automobile to anywhere in Europe.

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

Curious? So are we.

We look forward to receiving your online application with the following documents (as a single PDF) by 16 December, 2025:

• cover letter that contextualizes your scientific background, skills, and interests as well as articulates why you are interested in the group

• CV

• diplomas and course transcripts

• contact details of at least two referees

Additional dates:

• Initial Zoom call - 18 December 2025 : Shorlisted candidates will be invited for a brief introductory Zoom meeting.
• In- Person interview - 12 January 2026: Selected applicants will be invited to visit the lab for a full in-person interview.
• Latest start date: 14 April 2026.

Please submit your applications online. Applications via email will not be considered.

Further information can be found on our website. For questions about the position, please contact Prof. Platt ) (no applications).

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