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Field
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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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management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled experiments is also desired, but not
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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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, 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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-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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, 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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, or graph-based encodings in materials and molecular AI. Familiarity with frameworks for automated and reproducible workflows. Knowledge of governing regulations around privacy (e.g., HIPAA, ITAR), including
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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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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain