Research Engineer — ETH Zürich

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Research Engineer 100%, Singapore, fixed-term Drucken The Singapore-ETH Centre was established in 2010 by ETH Zurich - The Swiss Federal Institute of Technology and Singapore’s National Research Foundation (NRF), as part of the NRF’s CREATE campus. As ETH Zurich's only research centre outside of Switzerland, the centre has strengthened the research capacity of ETH Zurich to develop sustainable solutions to global challenges in Switzerland, Singapore and the surrounding regions. Set in Asia, in a rapidly urbanising region, the Singapore-ETH Centre aims to provide practical solutions to some of the most pressing challenges on urban sustainability, resilience and health through its programmes: Future Cities Lab Global (FCL Global) and Future Health Technologies (FHT) . The centre serves as an intellectual hub for research, bringing together principal investigators and researchers from diverse disciplines and backgrounds. To promote the exchange of ideas and expertise, our researchers actively collaborate with universities and research institutes and engage with industry and government agencies to translate knowledge to practical solutions to real-world problems. Project background The healthcare systems of the future must harness data effectively to support clinicians, allowing them to focus on patient care while leveraging AIto detect patterns beyond human perception, enhance diagnostic accuracy, optimise workflows, improve risk assessment and communication. Developing AI models that address these needs is particularly urgent in ageing societies, where rising patient numbers coincide with increasing workforce constraints. To do so, we are developing the AI for Science Instrumentation Gym, which is designed to bridge this gap by placing data-driven hypothesis generation at the center of its mission. It introduces a critical intermediate step: the tokenization and cartography of scientific data. Through tokenization, complex data is transformed into coarse-grained, interpretable units. Through cartography, these units are organized into latent spaces that can be explored as structured landscapes. In this way, machine learning becomes a tool for mapping high-dimensional data into forms that scientists can navigate, interpret, and use to generate new hypotheses. To build the AIS Instrumentation Gym, we are opening a set of Instrumentation Gym Lead positions. IGLs are data scientists and systems builders who design, implement, and scale the AIS Instrumentation Gym across its different levels (S/M/L). They form the infrastructure backbone of the ecosystem, enabling domain scientists and machine learning researchers to work with complex scientific data in a structured, scalable, and interpretable way. Job description As a Research Engineer, you will be one of the Gym Leads for the ML platform for the Scientific Instrumentation, and you will be working on:

• Data pipelines — ingestion, metadata schemas, and provenance tracking for electron microscopy, X-ray, and light microscopy datasets
• Model interfaces — modular Python APIs that let representation models, tokenizers, and downstream models be swapped and composed
• Baseline tokenization and cartography pipelines, built collaboratively with ML researchers and domain scientists
• LLM- and RAG-assisted interfaces for dataset navigation, workflow discovery, and user interaction
• Forward-compatibility to HPC — ensuring S-Gym and M-Gym components can be lifted into the L-Gym tier without fundamental redesign Profile
• Master’s degree on a relevant subject (e.g. Artificial Intelligence, Data Science, etc.)
• Strong Python and a modern ML framework (PyTorch preferred)
• Experience building data pipelines and reproducible ML workflows
• Comfort with GPU-based compute and basic scientific data formats
• Track record of building modular, maintainable software
• Interest in working at the intersection of science, ML, and systems enginee