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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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Village to calibrate and validate models. Investigating control strategies (e.g. predictive or machine learning approaches) to improve performance and reduce costs. Collaborating with industrial partners
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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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for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential inference algorithms as well as proofs of their correctness and efficiency
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and presented at international AI, software engineering, and security venues. The position is focused on discovering new errors using behavioral models learned from software. You will extend fuzzing
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unique opportunity to contribute to the technological foundations for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential
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suggested patches, easier to understand using interpretable AI using state machine models and LLM-based explanations. You will provide the community with the first tool for self-healing software that is
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multimodal deep learning models that integrate imaging and clinical data to personalize treatment and follow-up strategies. In the Netherlands, around 75% of patients with an abdominal aortic aneurysm (AAA
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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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