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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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plus. You enjoy working with complex, multimodal datasets and developing robust algorithms for continuous monitoring and predictive modelling. You are comfortable combining coding, data analysis, and
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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, neurodegenerative, autoimmune and infectious diseases. Leveraging innovative platforms such as novel biosensors, gene switches, genetic closed-loop circuits, and chemical structures that interface with and actuate
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). Applications from candidates without documented hands-on mouse experience will not be considered. The project also includes high-throughput metabolic profiling of genetic perturbations, computational data
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, including the use of CRISPR-engineered cancer cell lines and metastatic models. In addition, the project integrates high-throughput metabolic profiling of genetic perturbations, computational data analysis
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metabolic regulation. The mission of the group is to understand fundamental mechanisms regulating short- and long-term metabolic adaptation to genetic and chemical perturbations to find new and unconventional
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. This project aims to uncover how immune signalling reprograms metabolism in guard cells to drive stomatal closure and restrict pathogen entry. Job description Research focus: Molecular genetics in Arabidopsis
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-cancer drug discovery. Your main tasks will be: Developing new strategies to combine high-throughput metabolomics with 3D cell culture models Perform large chemical and genetic studies in cancer cell lines