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the 2026-27 academic year. We are seeking the best possible candidates without regard to subfield of specialtization. We invite applications from those who hold a PhD (or equivalent) in the relevant field(s
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: --- Preferred Qualification Education: PhD in climate science, engineering, or a related field. Experience: Knowledge and skills developed through 15+ years of work experience in a related job discipline
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of Chicago Law School. Responsibilities will span all stages of research, including collecting data of in both tabular and spatial formats, developing algorithms that clean and organize data, conducting
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). Contribute to image processing and algorithm development to support the identification of novel biomarkers and disease phenotypes. Write clean, efficient code primarily in Python and work with Bash/Slurm
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responsibilities will span all stages of research, including collecting data of in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical analyses, running
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using supervised and unsupervised machine learning/deep learning techniques for various research applications. Design and implement NLP algorithms and techniques for text preprocessing, feature extraction
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research activities, assists in preparing human subjects protocols, manages and analyzes data across multiple projects. Contributes to building traditional statistical models and machine learning algorithms
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with the goal of better understanding how places impact people. We develop machine-learning algorithms and non-linear measure of brain dynamics to quantify more vs. less effortful brain states. This is
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about the benefit offerings can be found in the Benefits Guidebook . Qualifications The position requires the PhD degree. The successful candidate will have a relevant teaching experience at
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related job discipline. Certifications: --- Preferred Qualifications Education: Master’s degree or PhD in Instructional Design, Education or Learning and Development. Preferred Competencies Demonstrated