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- Machine Learning in Biophysics About the Opportunity The SymBioSys Lab (www.simbiosyslab.com ) is seeking an independent and creative postdoctoral associate to support research on computational modeling
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intereactions in the broad field of glycoprotein-based therapeutic design. Strong background and experience with applications of machine learning (especially Explainable Artificial Intelligence – XAI) is required
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graph theory, network analysis, and/or machine learning modeling confer a significant advantage. This is a two to three year term position with opportunities for training in human systems neuroscience
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prototyping using HTML/CSS and JavaScript, along with knowledge of related libraries and frameworks such as React.js, D3.js, Material.UI, and Bootstrap (or a strong willingness to learn them). Additionally
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the Opportunity Conduct research on machine learning and control theory as part of a MURI grant. The work will combine tools from dynamical systems, control theory, and the theory of algorithms and
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(broadly defined), electrophysiology, genotyping, brain stimulation (tES, TMS), computational modeling and/or machine learning. For all our projects, we seek post-doctoral researchers who aim to take leading
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have a PhD in Physics, Molecular Biology, or related field and ample experience working with pore fabrication and computer control of hardware. Molecular biology experience is a plus. Candidate must have
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digital twins, machine learning, and PATs for biopharmaceutical products. The overarching goals of the project are to bring ML/AL innovation to facilitate biomanufacturing 4.0 and speed up production
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single-molecule imaging, genetic manipulation, whole-cell super-resolution microscopy, machine learning, and physical modeling to understand how neuron structures and functions emerge from the elementary
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. This project looks to accelerate the design of smarter occupant-centric building control algorithms that learn user behavior, are easy to use, and can correctly predict HVAC performance and power draw