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systems (such as RedCAP), Endnote files, and databases Demonstrated experience with data analysis, visualization, and building machine learning models in programming language such as Python or/and R
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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Computer Science, AI/ML, Computational Biology, Food Science with computational expertise, or a related field. Experience with natural language processing, machine learning frameworks (e.g., PyTorch
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holder must have: Expertise in advanced theoretical and computational electromagnetics, including high frequency asymptotics. Expertise in artificial intelligence and machine learning. Recent research
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the Section for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and
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engineered constructs. Learn more about the innovative work led by Dr. Chris Chen here: https://bdc.bu.edu/bdc-team/. What you’ll do: Independently conduct research on liver cell proliferation, expansion, and
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. Expertise in artificial intelligence and machine learning. Recent research experience in the development of first-principle wave models. Recent research experience in the development of numerical codes
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experience in the use and/or development of machine learned interatomic potentials would be seen as a significant asset. Previous experience of inelastic neutron scattering spectroscopy (or similar) would be
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integrating them with microfluidics as a standalone device. This is a great opportunity to learn new skills, contribute to assay development, and intellectually contribute to projects within a collaborative
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person will also supervise current graduate students working on breast tumor innervation, given she has also gained experience in this area during his/her PhD tenure. In addition, this individual will