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Field
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. As a hydro-focused center, the WERC conducts vital projects that turn sciences and engineering into actionable solutions. By integrating machine learning, sensing technologies, and predictive modeling
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develop public health-focused entrepreneurship opportunities in collaboration with new and on-going WashU programs. Develop and sustain an alumni network for FARM-supported students and postdocs. Monitoring
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that are to be conducted in the lab. Collecting and analyzing behavioral and EEG data for various research studies that are to be conducted in the lab. Â Writing computer code to implement computerized experiments
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the PI manage a lab of 15-20 graduate students, postdocs and technicians. Responsibilities will include interfacing with the university on compliance matters (e.g. IACUC and EH&S) and helping with
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of the Research Project(s): The goal of our research is to develop vaccines and therapeutics targeting human pathogens. Duties and Responsibilities: 20% Structure-based and machine-learning immunogen design for
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departmental spaces, room reservations, and key inventory. Other Duties as Assigned Provide administrative and logistical support to faculty, postdocs, and students. Assist with special projects that advance
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for eligible postdoctoral Associates to spend on health care, retirement, childcare, or other personal use. What you’ll do: Work efficiently and cooperatively to assist Postdocs, Graduate Students, and other lab
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- Knowledge of mathematical probability and statistics, and optimization methods - Knowledge of machine learning, including supervised and unsupervised learning, deep learning, and model evaluation - Knowledge
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representativeness - Knowledge of software engineering for AI applications - Knowledge of mathematical probability and statistics and optimization methods - Knowledge of machine learning including
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operational constraints and employee preferences, within rigorous optimization frameworks. Data science and machine learning: experience with data preparation, feature extraction, and preference learning