ETH Zurich

Two PhD positions in Biophysics and rheology of bacterial biofilms 100%, Zurich, fixed-term print Drucken The bioMatter Microfluidics Group of Dr Eleonora Secchi at ETH Zurich is seeking two PhD candidates. Our research focuses on uncovering the physicochemical mechanisms that control microbial surface colonisation and biofilm assembly, structure, and rheology. We use a broad spectrum of technologies in materials science, microbiology, and microfluidics, as well as advanced imaging techniques to address our questions. We are a highly interdisciplinary, international, and collaborative team of about 10 members, hosted within the chair of Prof. Roman Stocker in the Institute of Environmental Engineering. Project background The two PhD positions are part of a recently funded SNSF project aimed at systematically investigating nonlinear biofilm rheology, with emphasis on the role of extracellular DNA (eDNA). Biofilms are a ubiquitous form of microbial life with important implications in medicine, industry, and the environment. They are responsible for persistent infections, antibiotic resistance and biofouling, leading to economic costs of billions of dollars annually and thousands of deaths. Biofilms are microbial communities encased in a polymeric matrix that provides mechanical stability and protection from mechanical stresses through its viscoelastic properties. While the linear viscoelastic response under small deformations is well characterised and recognised as a virulence factor, the response to large deformations remains poorly understood. In particular, there is a lack of systematic investigation of the nonlinear regime, where externally applied loads can induce stress-hardening and stiffening of the biofilm matrix. Recent findings from our group suggest that eDNA may play a central role in the stress-hardening of biofilms. We hypothesise that this behaviour arises from the entropic elasticity of the eDNA network, a mechanism well described in polymer physics but largely unexplored in living biofilms. This could enable both short- and long-term adaptation to flow fluctuations. While initial experiments are consistent with this hypothesis, further investigation is required to validate the underlying molecular mechanisms and to determine whether stress-hardening is specific to streamers or constitutes a broader feature of biofilm mechanical adaptation across different morphologies. This project will test these hypotheses through a combination of structural, biochemical, and rheological analyses of the biofilms and mathematical modelling, with the potential to reveal fundamental principles of biofilm resilience. Job description Experimentally investigate nonlinear rheology and stress-hardening in bacterial biofilms with different morphologies using custom microfluidic and rheometry platforms. Quantify the role of eDNA and its interactions with biofilm matrix components using mutant libraries, enzymatic/antibody assays, and controlled physicochemical conditions; assess incorporation of exogenous DNA into biofilms and its impact on morphology and mechanics. Develop and apply advanced fluorescence/confocal imaging approaches to resolve biofilm network structure and eDNA conformation in situ. Contribute to the development of a numerical predictive model of biofilm mechanics. Collaborate within an interdisciplinary team and with external partners; communicate results through publications and presentations. The tasks include wet-lab experimentation, project management, numerical modeling and teaching duties. Position details: Start date: February 1st, 2026, or by agreement Fully funded PhD position (approximately 4 years). Final admission to the doctoral programme follows a successful Aptitude Colloquium at the end of year 1; contracts are extended annually. Profile The ideal candidate holds a Master-s degree in: physics, biophysics, materials science, microbiology or a related field with a strong interest in interdisciplinary research at the interface between soft-matter physics and microbiology Experience in experimental work such as microfluidics, rheology, microscopy, or culturing microbes and familiarity with data analysis and quantitative modelling is highly valued The candidate should be motivated to work both independently and collaboratively within an international research environment and to contribute actively to teaching activities Workplace Workplace We offer Training opportunities Perspectives for career development Support programmes (e.g. mentoring) and networks Your (team) culture and team composition Working environment and employment conditions Your commitment to diversity (e.g. flexible working hours, possibility for part-time work, home office) chevron_right Working, teaching and research at ETH Zurich 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 by November 30th, 2025 , including the following documents: Curriculum vitae Cover letter (including motivation, research interests and possible start date) Full transcript from undergraduate studies (both Bachelor and Masters) Copy of Master-s or Bachelor-s thesis (PDF) At least two reference letters Applications will be reviewed as they are received. Please note that we only accept applications submitted through our online application portal. Applications sent by email or post will not be considered. For questions regarding the position (but not for submitting applications), please contact Dr Eleonora Secchi (esecchi@ethz.ch). About ETH Zürich 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.

Experienced Researcher in Development and application of computational methods for functional genomics 100%, Basel, fixed-term print Drucken 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. chevron_right Working, teaching and research at ETH Zurich 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