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, and evaluation The ideal candidate will lead their own project, and also collaborate with and support 1-2 PhD students on their projects. The ideal candidate will also be interested in learning to write
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phenomenology, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world
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the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
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original research on clandestine printing networks using computational tools Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals) Contribute
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performance with classical methods. Qualifications PhD in Physics, Computer Science, Applied Mathematics, or related fields. Strong background in at least one of the following: machine learning, quantum
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developing and validating machine learning-based predictive models using multimodal data (neuroimaging, clinical, biomarker, and demographic data). Support the design and implementation of computational
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accordance with the Boston University Postdoctoral Compensation Guidelines and the Postdoctoral Scholars Policy. To be considered, applicants must hold a PhD in epidemiology, biostatistics, computational