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Field
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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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vulnerabilities. This role sits at the intersection of AI for security, AI security, and computer architecture, contributing to a first- of-its-kind security framework for next-generation Hw/Sw computing systems
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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
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and to contribute to collaborative papers and grant proposals. Responsibilities • Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. • Lead
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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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climate will warm and recover in a net-zero future. As part of this project, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques
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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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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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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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modelling, machine learning, growth mixture modelling). Excellent skills in statistics and advanced quantitative data analysis, including strong skills in command driven programming languages (e.g., STATA, R