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) • Big data pipelines, distributed computing, and geospatial data processing • Python, R, SQL/NoSQL, containerization (Docker), Kubernetes • API development and web-based analytics tools • Systems
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simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling Large-scale computing, cloud-native analytics workflows, and data engineering for mobility platforms AI/ML
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-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs, including prompting, fine-tuning, or evaluation Machine learning
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quantitative analytic methods. Core responsibilities include: Conducting data analysis for ongoing projects. Developing independent and collaborative publications by accessing available longitudinal data
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some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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studies. Lifespan developmental perspectives linking psychological factors to health. Cutting-edge quantitative analytic methods. Core responsibilities include: Conducting data analysis for ongoing projects
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with Prof. Aditya Vashistha and collaborate with faculty and students participating in the Cornell Global AI Initiative Qualifications Applicants should have: A PhD in HCI, AI, NLP, Information Science
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molecular biology techniques such as DNA extraction, PCR, or qPCR. Experience analyzing biological data using command line tools, R, SAS, or similar analytical software. Familiarity with bioinformatic
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of the experiment, from data analysis to detector operations and HL-LHC detector upgrades. The successful candidate is expected to engage actively in CMS measurements as well as the smart pixels program (https
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(e.g., transcriptomics, proteomics, metabolomics) - Microscopy methods, FT-IR spectroscopy, or other analytical imaging techniques - Analysis of next-generation sequencing data - Programming skills (e.g