Postdoctoral Fellowship (27) at ETH Zurich, Switzerland

FHT2 is an NRF-CREATE programme co-funded by the National Research Foundation (NRF), ETH Zurich, and NHG-NTU, and represents the second phase of the FHT initiative launched in 2020. The programme brings together psychologists, neuroscientists, computer scientists, engineers and clinicians to develop next-generation digital health solutions.

The postdoctoral researcher will join the Monitoring and Supporting Cognitive Health in the Community project. This project focuses on the development and deployment of an AI-powered mobile platform designed to assess, monitor, and support cognitive health during real-world navigation. The platform integrates cognitive and navigation assessments, smartphone sensor data, and AI-driven conversational agents to detect early signs of cognitive impairment, deliver personalised support, and promote ageing in place. The project is closely aligned with Healthier SG and the Action Plan for Successful Ageing, with clear pathways for translation through partnerships with organisations in Singapore.

Living cells such as neurons can be viewed as biological systems with complex, dynamic, and multi-functional behaviors through their billions of years of evolution. Interfacing with living cells using advanced micro-/nano-electronics enables new sensing and actuation opportunities but requires robust biological protocols and stable bioelectronic interfaces.

In parallel, biomolecules and their complexes can be probed and controlled using CMOS micro-/nano-electronics, enabling applications such as molecular sensing (DNAs/proteins), surface functionalization, electrochemical reactions, and molecular synthesis.

Our ETH IDEAS group has developed many generations of CMOS cellular/molecular platforms with dense arrays of electrodes and multi-modality active pixels for sensing and actuation. Some of these platforms focus on long-term culture and sensing of biological systems, including neurons, human stem-cell-derived cells, and animal-based cells. Others focus on molecular or chemical functionalities, including electrochemical measurements and on-chip molecular processes.

Across these diverse projects, a central requirement is bio-experimental expertise in both cellular and molecular/electrochemical workflows. This postdoctoral position supports research across these applications and contributes to the development and operation of CMOS bioelectronics and biosensors at ETH IDEAS group.

The postdoctoral researcher will lead and develop research activities on next-generation membrane processes. Responsibilities may include:

• Experimental evaluation of membrane performance in gas and liquid separations (e.g., carbon capture, methane purification, water treatment)
• Fabrication and characterization of nanoporous and polymeric materials
• Theoretical and computational modeling of transport and energy efficiency
• Supervision and mentoring of doctoral and master’s students
• Preparation of scientific manuscripts and contribution to research proposals

The postdoc will work closely with Prof. Anthony Straub to carry out these responsibilities. Opportunities exist to interface with industrial collaborators and national research initiatives, such as NCCR Separations.

We are seeking an exceptional and highly motivated Postdoctoral Researcher to lead research on the in-hospital deployment of Large Language Models (LLMs). The focus of this position is the development, validation, and safe integration of locally hosted LLMs for automated coding of pediatric diagnoses from electronic health records (EHRs), with the goal of enhancing research capabilities and clinical data usability. 

This position is embedded in a close collaboration between ETH Zurich’s Medical AI Lab and the University Children’s Hospital Basel (UKBB), enabling direct interaction with clinicians, clinical coders, and hospital IT infrastructure.

Forest dynamics models increasingly need to represent climate-sensitive ecological processes while remaining computationally robust, transparent, and reproducible. The model Forest Studio is designed as a modular modelling environment that integrates ecological theory, empirical data, and modern scientific programming workflows.

This position focuses on advancing the implementation of ecological processes, such as regeneration, mortality, and natural disturbance regimes, while ensuring rigorous testing, documentation, and integration within a collaborative software development environment.

Our group addresses key challenges in quantum science by building and exploiting hybrid quantum systems consisting of individually controlled atoms and atomic ensembles.

Current research directions include:

• Demonstrating scalable architectures for fault-tolerant quantum information processing, featuring reconfigurable local control, rapid quantum non-demolition readout, and fast error-syndrome extraction
• Developing strong atom–light interfaces to enable quantum interconnects and modular quantum systems
• Investigating non-equilibrium quantum many-body dynamics and quantum information dynamics in programmable atom arrays

In cognitive science, it is common practice to use cognitive models to understand human thought processes – including human learning. Large language models create new opportunities for cognitive modeling. In the past, the group has created cognitive models (e.g., artificial neural networks, see Rau et al., 2019) that mimic specific human learning processes. We then used these models as “digital twins,” testing whether specific types instructional interventions can enhance knowledge acquisition for the model. A human experiment showed that the optimized intervention that resulted from the digital-twin experiments indeed also improved human learning.

The group now aims to examine how large language models can serve as digital twins for human learning. Digital twins are particularly useful in cases where many possible learning interventions exist or when the interventions are associated with risks. The latter is particularly relevant for the team’s ongoing research on educational interventions for misinformation.

The Experimental Mineral Physics (EMP) group in the Institute of Geochemistry and Petrology (Prof. Motohiko Murakami) invites applications for a postdoc position in ultrahigh-pressure mineral physics and geophysics.

Our research focuses on the ultrahigh-pressure experimental mineral physics to explore the internal structure, mineralogy and evolution of the Earth and the other planets.

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Postdoc position in development of protocols for isolation of microbial strains for sustainable protein production

Deadline : Open Until Filled

View details & Apply

Many small riverine organisms rely on flow‑mediated processes to complete their life cycles. One such process is drift, a fundamental mechanism of downstream dispersal for aquatic invertebrates and fish larvae. Although the importance of drift is well established, many of its underlying dynamics—particularly its spatial and temporal variability—remain poorly quantified. This knowledge gap largely stems from limitations in current sampling approaches, which rely primarily on drift nets and are both labour‑intensive and inherently limited in resolution.

Addressing these limitations requires a game change in how drift is observed and quantified. There is a pressing need for non-invasive, cost-effective, and broadly deployable technologies capable of providing long-term (weeks to years), highly resolved (hourly‑scale) data on species presence, abundance, and movement patterns in rivers and streams. To address this gap, this project aims to realise the Riverine Organism Drift Imager (RODI)—a unique technology developed by our group—as a fully operational, camera-based underwater imaging system. RODI is designed to continuously detect, classify, and quantify drifting organisms in situ, thereby enabling drift studies at unprecedented temporal and spatial scales. By bridging engineering, imaging, and aquatic ecology, the project seeks to transform how riverine drift is studied, monitored, and applied in both research and practice.

Prof. Dr. Nicola Zamboni’s team studies how metabolism functions and is regulated. The group focuses on systems in which metabolism or metabolites influence phenotypic differentiation and biomedical processes, particularly in complex or technically challenging settings. To address these questions, the lab develops and applies new methods for metabolomics, lipidomics, and ¹³C metabolic flux analysis, as well as for data processing, integration, and interpretation. Current research directions include, for example, applying DIA-based strategies in large-scale studies, combining electron- and photon-activation methods for structural analysis of metabolites and lipids, creating metabolomics knowledge graphs, and inferring fluxes from MS data. The group collaborates with mass spectrometry vendors and technology partners to evaluate emerging instrumentation and software and translate them into robust, scalable workflows.

Alongside method development, the Zamboni Lab is tightly embedded in numerous collaborations with academic, clinical, and pre-clinical groups, for which it provides measurements as well as expertise in the interpretation and integration of metabolomics data. The lab is a pillar of the Swiss Multi-Omics Center (SMOC) and contributes state-of-the-art metabolomics and lipidomics capabilities to large consortia, including activities connected to personalized health.

This position is ideal for researchers who want to push scientific boundaries, build a visible and independent research profile, and work on problems that matter at the interface of AI and biology. You will have the freedom, resources, and intellectual environment to turn bold ideas into impactful science.

We are looking for people with drive, creativity, and leadership potential. If this sounds like you, we would be excited to hear from you.

This position is part of the ERC Starting Grant project “The Politicization of Low-Carbon Technologies (PolTech)” funded by the European Research Council. The PolTech project aims to explore the drivers and impacts of technology politicization. Specifically, it studies the technology-politics interaction in the context of various LCTs, such as solar PV, wind turbines, electric vehicles, heat pumps, and more. These LCTs are crucial for achieving global climate change mitigation targets, but their adoption is often affected by intense politicization. The project examines how varying levels of LCT politicization affect technology adoption, industrial development, policy design, and its socio-technical and political feedback effects.

The project is embedded within ETH Zurich’s new Einstein School of Public Policy, providing excellent opportunities for collaboration with leading scholars at ETH Zurich, EPFL, Technical University of Munich, Stanford University, and others, as well as policymakers in Switzerland, the EU, and other OECD countries.

Tuberculosis (TB) kills an estimated 1.25 million people each year, making it the single deadliest infectious disease. Unfortunately, TB disproportionately impacts low- and middle-income countries (LMICs); 98% of the global TB cases occur within LMICs, leading to devastating effects. Furthermore, the proliferation of this disease has resulted in widespread misuse of antibiotics, leading to the development of substantial drug resistance. Indeed, in the worst affected regions, drug resistance among recurring TB infections has risen above 50%. 

Most TB deaths are preventable if diagnosed early. However, almost a quarter of all TB cases go undiagnosed. Similarly, the proliferation of drug resistance in TB can be partially attributed to a scarcity of effective methods for identifying resistance markers, which results in poor antibiotic stewardship. Unfortunately, contemporary diagnostic technologies have proved insufficient for diagnosing TB and associated drug resistances, particularly at the point-of-care (PoC). Few technologies exist that can quickly and accurately diagnose TB and simultaneously determine resistances, and those that can are large and expensive, precluding their use in LMICs. They are also overly reliant on sputum samples, which can be difficult to obtain, particularly in low-resource settings. 

In this project, we want to develop an affordable, portable, and rapid diagnostic platform technology that can multiplex 14 targets for TB and associated markers for drug resistance from a single sample. This technology will be paper-based and leverage electrochemical signaling to facilitate miniaturization and provide quantitative readouts of disease. These paper-based tests will be constructed using a recently patented technology from ETHZ, namely, the laser-induced graphenization of cellulose. This manufacturing process is affordable, scalable, and rapid, making it ideal for constructing PoC devices. This technology will be combined with novel CRISPR–Cas-based biosensing assays, specifically engineered to enable the detection of single-nucleotide polymorphisms (SNPs) associated with drug resistance. To facilitate deployment at the PoC, we will leverage the facilities and expertise of CSEM to integrate these technologies into a highly affordable cartridge & reader system. The research team will be supported by the Swiss Tropical and Public Health Institute and the National Center for Tuberculosis and Lung Disease in Georgia, who will validate the technology in patient samples and perform a small pilot study. 

• You will lead the computational and theoretical investigation of damage evolution associated with cavitation in soft materials under high-rate loading. 
• The work will focus on developing physics-based models that connect behavior across scales, from the polymeric network and mesoscale structure to the macroscopic continuum, supported by consistent coarse-graining and homogenization strategies. 
• Your responsibilities include formulating constitutive and damage laws that capture cavitation-driven processes, implementing and verifying robust large deformation solvers, and performing rigorous verification and validation using datasets provided by collaborators in Prof. Christian Franck’s group at the University of Wisconsin–Madison.
• The position provides a stimulating environment for scientific growth and collaboration.