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Field
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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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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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. 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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. 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
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expected to actively contribute to the project, work collaboratively as part of the research team, and perform the following major tasks: Conduct theoretical analysis, algorithm development, and simulation
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and genomic data from distinct studies. Maintaining accurate code repositories for quality control and analytical algorithms Manages storage drives and maintains datasets. Required Education and
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-seq, single-cell RNA-seq, and spatial data) and experience in multi-omics data integration. Has the ability to develop algorithms and code for innovative multi-omics data analysis. Baylor College
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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