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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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executing experiments to interrogate host-microbe interactions, analyzing and interpreting microbiome sequencing and/or metabolomics data, developing novel computational or analytical approaches
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include designing and executing experiments to interrogate host-microbe interactions, analyzing and interpreting microbiome sequencing and/or metabolomics data, developing novel computational or analytical
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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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(Investigating how climate change may increase risk of zoonotic spillover by changing human and wildlife movement, stress, and behavior). This position requires a PhD in ecology, disease ecology, epidemiology
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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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biological mechanisms driving spillover to understanding distal anthropogenic drivers such as land use change and climate variation. Successful applicants will have a PhD in systems science, systems biology