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postdoctoral researcher for pioneering research at the convergence of artificial intelligence (AI) and materials science. The ideal candidate should possess expertise in scientific machine learning (SciML
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will apply state-of-the-art machine learning algorithms and custom disease-relevant genomic datasets (e.g., coronary artery single-nucleus chromatin accessibility and RNA sequencing) to develop targeted
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, Economics, or a related field, earned within the past six years Strong computational and statistical skills Experience with large-scale data analysis and machine learning Proficiency in scientific programming
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into normal and malignant hematopoiesis, and to establish certain protein-RNA hubs as actionable therapeutic targets and molecular markers, contributing to the emerging field of precision medicine. To learn
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. Postdoctoral Research Associates will conduct their own research, participate in PCD programming, and teach one undergraduate course per semester, either “The American Political Tradition” (PLAP 2250
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Machine Learning, Graph Modeling and Mining. Similar or related fields will also be considered, however programming fluency is required. The selected candidate must hold a PhD at the time of appointment
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to support University projects or programs. They focus primarily on learning and applying high-level data projections and statistical analysis. They independently manage the design and programming of all data
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world
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teaching experience and/or teaching philosophy and courses you would like to teach Contact information for three references (references will only be contacted for those who are short listed) ** Note
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should have demonstrated interest in early childhood education research and policy. Particular interests in improving access to high-quality early childhood learning opportunities at scale and/or