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Field
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foundation models and agentic systems and demonstrated capability to produce workable solutions from theoretical formulations Substantial publication history in top computer vision/machine learning venues
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works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual
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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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managers. The candidate will become part of an adaptation-oriented team, which allows many opportunities to collaborate internally and with external partners. They will learn from interacting directly with
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. Prepare and maintain classroom and lab (i.e., kitchen and restaurant) for applicable learning activities. Responsible for coordination and associated paperwork for 620 West and enterprise activities. Assist
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behaving mice, and advanced modeling + machine learning analyses. Please read more about our research at www.apostolideslab.org . Key questions we want to answer are: How do neural circuits extract
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machine learning model development; Experience designing research projects and managing project delivery; Experience processing, documenting, and analyzing data sets; Ability to excel in a highly