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. Required Qualifications: PhD in statistics, economics, computer science, operations research, or related data science fields Strong data science skills, including experience working with large, complex data
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. Required Qualifications: Doctoral degree (PhD) conferred by start date Demonstrated experience with analysis of large health databases Training and experience in machine learning and deep learning methods
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large-scale optimization and theoretical combinatorial optimization, algorithmic reasoning remains a significant challenge for artificial intelligence. Our lab’s research is driven by the observation
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include a PhD in Computer Science, Artificial Intelligence, Natural Language Processing, Human-Computer Interaction, or a closely related field. Candidates should have demonstrated expertise in Large
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care actually happens, and how it can be made better. This is a role for someone who’s excited to work with big, messy, real-world data — and who wants to do more than just build models. We’re looking
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verbal. Preferred Qualifications: Experience combining large clinical and research data sets. Interested and comfortable working with pediatric patients with special needs. A track record of publications
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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen
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on hotspot detection. This modeling work is well supported by large-scale primary datasets, including survey-based, parasitological, serologic, and genomic data. Relevant methodologies include mechanistic
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substantial component of the work focuses on large scale empirical research in international macroeconomics and finance. The Global Capital Allocation Project (GCAP) Lab mixes data, economic theory, and
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cultures to uncover the biological mechanisms underlying resilience in APOE4 carriers. EDUCATION AND EXPERIENCE: ● PhD in neuroscience, life sciences Required Qualifications: KNOWLEDGE, SKILLS, AND ABILITIES