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phone, keyboarding, filing, and fax machine skills); knowledge of medical terminology; and interpersonal skills. Does this position have supervisory responsibilities? No Preferred Education/Experience Two
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The candidate will be involved in the development and implementation of machine
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Master’s degree with 1 year or more experience. Exceptional leadership skills and excellent computer skills, written, and oral communication skills are preferred. Knowledge/Skills/Abilities Successful
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. Demonstrated ability to apply theoretical knowledge of science principals to problem solve work. Ability to maintain detailed records of experiments and outcomes. General computer skills and ability to quickly
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Science, or related field Knowledge of flexible antennas, wireless communications, and machine learning Good skill set in signal processing and optimization techniques Proficiency in Python for modeling and machine
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*• Experience in Python or another programming language (projects, GitHub repositories, courses, scientific use).• Training or experience in machine learning and data science applied to environmental or energy
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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/13 04:59 AM UnitedKingdomTime** (posted 2025/10/21 05:00 AM UnitedKingdomTime, listed until 2026/04/22 04:59 AM
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California State University, San Bernardino | San Bernardino, California | United States | 8 days ago
provides the required knowledge and abilities. Required Qualifications: Knowledge: Thorough knowledge of general office methods, practices and procedures. Thorough knowledge of, or the ability to acquire
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candidates in the area of Business Analytics, with particular interest in those applying machine learning and deep learning methods to business domains, such as HR analytics, marketing analytics, and
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture