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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical
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classes and their roles in scientific applications, such as deep neural networks (DNNs), convolutional neural networks (CNNs), transformer models, and graph-based neural networks. Familiarity with software
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scientific writing skills. Design and performance of experiments, creating graphs, knowledge of statistics, interpretation and dissemination of data. 3-years of mentorship of junior technicians and trainees
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, Optimization, and AI • ML/AI for mobility prediction and optimization • Graph algorithms, network science • Spatiotemporal modeling • Operational research for mobility and infrastructure • Real-World Practice
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, or materials informatics. Familiarity with explainable AI or counterfactual explanation methods. Experience with molecular dynamics data, graph neural networks, or multi-component system modelling. Track record
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. Demonstrated experience with electrophysiology data analysis (human or animal). Experience in graphing, statistical analysis and data management skills. Certifications/Licenses Required Knowledge, Skills, and
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-doctoral associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be
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learning Broad familiarity with geospatial programming libraries Preferred Knowledge, Skills, and Abilities: Non-LLM foundation model expertise Time Series Foundation Models Expertise with Graph transformers
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Large Language Models (LLMs). The position will also involve creating quantitative evaluation frameworks to assess the quality, realism, and reliability of generated data, as well as integrating graph
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, containerization (Docker), Kubernetes API development and web-based analytics tools Systems, Optimization, and AI ML/AI for mobility prediction and optimization Graph algorithms, network science Spatiotemporal