Postdoctoral position in Development and application of computational methods for functional ge[...]

Eidgenössische Technische Hochschule Zürich; England; On-site; GBP 38,000 - 52,000; Full time

Job summary

A leading global university is seeking a Postdoctoral Associate in Basel to develop computational methods for experimental functional genomics. The role involves contributing to innovative screens and modern analysis techniques in a collaborative environment. The ideal candidate holds a PhD in a relevant field and possesses strong skills in Python and R. This position offers an opportunity to work at the forefront of bioinformatics, technology, and collaborative research.

Benefits

Access to cutting-edge technology

Collaborative research environment

Qualifications

• PhD or equivalent in relevant field.
• Ability to communicate effectively in English.
• Experience in analysis of deep sequencing and single-cell data.

Responsibilities

• Develop analysis methods and advise on experimental design.
• Build and maintain reproducible analysis pipelines.
• Contribute to biological manuscripts and present results.

Skills

Strong Python and R skills

Software-engineering practices

Analyzing deep sequencing and single-cell data

Strong grounding in statistics

Bioinformatics workflow design

Education

PhD or equivalent in Bioinformatics, Computational Biology, Computer Science, Applied Statistics

Tools

Snakemake

Nextflow

Python

R

Job description

Postdoctoral position in Development and application of computational methods for functional genomics

The Laboratory for Biological Engineering (Prof. Randall J Platt) at 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 perturbation datasets, requiring the development of sophisticated approaches and methods incorporating facets such as 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 for the novel data modality generated by Record-seq.

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, and interpreting findings.

The candidate will primarily be engaged in the following activities:

• Develop analysis methods and advise on experimental design (target gene selection, power analyses, guide-library design, readout selection).
• Build, maintain, and document reproducible analysis pipelines (Python/R; Snakemake/Nextflow preferred) for novel experimental methods.
• Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling.
• Contribute to biological manuscripts and 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.

The candidate must be able to communicate effectively in a highly interdisciplinary and international environment, which includes 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).
• Demonstrated experience analyzing deep sequencing and single-cell data (e.g., Scanpy/Seurat, count models, batch correction, differential analyses).
• Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles.
• Bioinformatics workflow design (Snakemake/Nextflow) and HPC/cloud computing.

Prior experience in the following is a major plus:

• CRISPR screen analysis (pooled or single cell), guide demultiplexing, library design
• Metagenomics, meta-transcriptomics and metabolomics data analysis and familiarity with gut microbiome research.
• Machine learning for genomics (representation learning, generative models, causal inference).
• Multi-omics integration (scRNA-seq + CRISPR barcode/perturbation; metagenomics/meta-transcriptomics/metabolomics; transcriptomics/proteomics).
• Genome-scale metabolic modeling applied to single microbes and their communities.

Workplace

We offer

The position is located in the Department of Biosystems Science and Engineering (D-BSSE) of 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). 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.

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.

Curious? So are we.

We look forward to receiving your online application with the following documents (as a single PDF) until 10 October 2025:

• Cover letter that mentions your scientific interests and why you are interested in the group
• CV
• Diplomas and course transcripts
• Contact details of three referees

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about the Department can be found on our website. Questions regarding the position should be directed to Prof. Platt, [e-mail] (no applications).

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.