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these machine learning-based proxies together with a postdoctoral researcher working in this project (see below), leveraging data from experiments in our project. Third, you will explore how local connection
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theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development
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simulations. Job Description Are you passionate about bridging computational modeling with clinical cardiology to solve real-world healthcare challenges? We're seeking a PhD candidate to develop innovative
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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evaluate them alongside newly developed approaches. Integrating methods such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary
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position within a Research Infrastructure? No Offer Description We are seeking an enthusiastic and motivated PhD candidate with a strong interest in microfluidics, hardware development and NMR spectroscopy
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researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be
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geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By