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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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to preventive interventions, decision support to implement mental health interventions, longitudinal data analysis, machine learning/NLP/AI, integrative data analysis, and related grant writing. This candidate
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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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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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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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autoimmune diseases. QUALIFICATIONS Applicants must have PhD and/or MD (or equivalent) degree in hand by start date. Preferred applicants will have experience in immunology, metabolism, immunometabolism, and
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of national research infrastructures. The ideal candidates will have a PhD in a discipline closely related to computational social science by date of appointment (e.g. network science, computer science, data
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will have a PhD with experience in relevant areas of biology, first-author publication(s), and the ability to work well in a highly interactive, creative, and collaborative environment. Postdoctoral
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from the NIDDK and focuses on investigating the role that T1D risk variants play in islet autoimmunity through advanced single-cell techniques. The lab seeks an independent, highly motivated PhD-level
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. Minimum Qualifications: PhD in Cognitive/Developmental Psychology/Neuroscience or related field. We are also open to applications from candidates who are graduating in spring/early summer 2025 with