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Field
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help develop new computational models that integrate molecular reaction networks with AI/ML algorithms in order to predict patient-specific cardiac remodeling and heart disease outcomes across human
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fusion of sensor data and energy harvesting output, enabling adaptive and energy-aware data acquisition from textile-based wearable systems. Build adaptive algorithms that dynamically optimise data
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and efficiency of life sciences research. Developing the algorithms, infrastructure, and governance necessary for such analysis can simultaneously enhance hypothesis generation, computational modeling
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constraints. The postdoc will work at the interface of reinforcement learning and computational epidemiology, focusing on the development of new reinforcement learning algorithms. The project will consider
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validate a comprehensive genetic panel for the molecular diagnosis of pituitary diseases. Using next-generation sequencing (NGS) technologies and a bioinformatics algorithm, the panel will identify genetic
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develop algorithms for this purpose. The group collaborates with several national and international research groups, edits one of the major journals on data privacy (Transactions on Data Privacy), and has
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validate a comprehensive genetic panel for the molecular diagnosis of pituitary diseases. Using next-generation sequencing (NGS) technologies and a bioinformatics algorithm, the panel will identify genetic
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intelligent control and aerial robotics for navigation in uncertain environment. You will be mainly responsible: for implementation of machine-learning algorithms for unmanned aerial vehicles; validation
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with Prof. Mauro Maggioni on problems that include: analysis and algorithms on graphs, geometric analysis of high dimensional data sets, dynamic data sets, scientific machine learning, high-dimensional
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cleaning, filtering, etc.).•Expertise in data fusion and relevant algorithms (deep learning, generative AI, kernel methods, Bayesian methods). •Preferably, experience with high-content imaging or cell