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economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis. Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate
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regression, Cox proportional regression model, Poisson regression model) and Big Data analytics (e.g., machine learning, artificial intelligence [AI], text mining, generative AI) Knowledge/Skills/Abilities
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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experience in computational and machine learning approaches for use in the quantitative measurement of human learning and its associated behaviors and motivational constructs. A strong applicant will hold Ph.D
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Senior Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree or foreign equivalent degree. Three years of research experience in machine learning and
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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the tissue. Analyzing research data using different computer software Generation of preliminary data for grant applications. Writing drafts of manuscripts. Coordinating with other investigators for lab
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issues. Proficiency in urban modeling tools such as MATLAB, Python (especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning
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) Assist in original research on clandestine printing networks using computational tools Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals