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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap camera footage for wildlife monitoring and conservation efforts. Job Responsibilities
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Frankfort, KY Grade Level 50 Salary Range $81,640-138,798/year Type of Position Staff Position Time Status Full-Time Required Education MA Click here for more information about equivalencies: https
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working at NYU please visit our website at: http://www.nyu.edu/about/careers-at-nyu.html . NYU aims to be among the greenest urban campuses in the country and carbon neutral by 2040. Learn more at nyu.edu
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). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through
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particular the research lines of Professors Seppe vanden Broucke (e.g., applications of deep learning, graph learning, geospatial analytics, process mining), Frederik Gailly (e.g., ontologies, knowledge graphs
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distribution. Develop and enforce procedures that align automation systems with daily logs, conduct daily/weekly checks on the video server and digital asset management system, and acquire program files from
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communication skills. Proficiency in developing deep learning models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in
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with Customer Relationship Management (CRM) or talent management systems. Knowledge, Skills, and Abilities (KSAs) Deep knowledge of experiential learning models, e.g., internships, co?ops, clinical
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for Machine Learning (AIML) is the largest university‑based machine learning research group in Australia and the country’s first institute dedicated to advancing machine learning, computer vision, deep learning