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Field
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statistical machine learning techniques to mine self-reports and sensor data to gain new insights towards assessment and longitudinal monitoring of bipolar disorder; b) work on sleep datasets exploring
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the possibility of extension. For more details on our research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu For questions, please email mweber
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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of immune cell function. These projects are focused on making safer and more effective cell therapies (e.g., CAR-T) and gene therapies for cancer and beyond. We are an interdisciplinary lab spanning
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the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target
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fluid dynamics. The successful candidate will be expected to work on all or a subset of the above topics, be proficient in working with large data-sets (observational or numerical), machine learning, and
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more about AI at Princeton, please visit https://ai.princeton.edu
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analyzed with various Machine Learning and Data Science techniques to assess the dynamics involving case processing and costs. The first phase of the research will organize general data from the lawsuits