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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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experimental cycle from real-time X-ray measurements to post-experiment reconstruction: Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets. Implement
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, including bioacoustics algorithms developed in the team. What you will do Conducting rigorous research at the intersection of ML and wildlife bioacoustics; Actively participating in regular group and one
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activities: • Develop a comprehensive model for selected structures, utilizing finite-difference time-domain (FDTD) electromagnetic simulations and iterative algorithms. • Create a physics-based generative
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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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. The work will include algorithm design, prototype implementation (e.g., in MATLAB/Python), deployment to robotics applications; onboard computational hardware using the Robot Operating System), and
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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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. Developing the algorithms, infrastructure, and governance necessary for such analysis can simultaneously enhance hypothesis generation, computational modeling, and post hoc support for laboratory studies and
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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
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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