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Field
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data analysis is required, particularly in the handling and interpretation of data arising from physical experiments or computational models relevant to nanoscience. Teamwork; Critical thinking and
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scientists on, e.g.: Developing self-supervised learning frameworks to extract features from unlabeled high-resolution microscopy data Training and evaluating segmentation models for detecting and
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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explore how to combine data-driven and model-based approaches to assess the quality of returned parts, monitor in-process performance in real time, and support intelligent decisions on remanufacturing
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a
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job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The twin crises
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genetic, phenotypic, and environmental data, testing when and how evolution can be forecast. As a PhD student in our group, you will gain hands-on experience in computational and mathematical modeling and
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physical and numerical modelling. Feel free to reach out to the project supervisors if you have any questions. Entry requirements: The ideal applicant will be enthusiastic and self-motivated with a first
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Starting Date 5 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
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Meritorious will be considered experience in: - Microscopy-based imaging - Immunohistochemistry - Work with clonal cell lines - Work with other model organisms such as D. melanogaster or D. rerio You are a