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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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staff in exhibit preparation, including object research, selection, mounting, and installation as needed. Fellowship Learning Goals Learn and follow recognized standards and policies for collection
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who shares our values and who will support the mission of the university through their work. Qualifications: Ph.D. in a relevant field such as Learning Sciences, Human-Computer Interaction, Education
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statistical software. Learning Objectives: Learn about the implementation of the application of machine learning methodologies in plant phenotyping and genotyping for the sugarcane molecular biology lab. Learn
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TRAINING: Literature review on anomaly detection in network data; Using deep learning to detect anomalies in network data flows.; 4. REQUIRED PROFILE: Admission requirements: Degree in Computer Engineering
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Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill
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who shares our values and who will support the mission of the university through their work. Qualifications: Ph.D. in a relevant field such as Learning Sciences, Human-Computer Interaction, Education
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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domain in the design of deep learning algorithms for cardiovascular disease detection. 4. REQUIRED PROFILE: Admission requirements: Master's degree in Biomedical Engineering, Computer Engineering