420 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"Iscte-IUL" positions at Virginia Tech
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given to candidates in the areas of artificial intelligence, machine learning, natural language processing, and human-computer interaction. We are seeking candidates motivated to contribute to a collegial
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into computer programs such as Word or Excel, and ensure that electronic data are backed up on at least one external medium. Additional duties will include office work. Much of the field work needs to be done
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees
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orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants
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ability to lead groups, plan, implement, facilitate, teach, and evaluate educational information programs; knowledge and use of computer technology in educational programming and management and presentation
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broad range of research areas. We are interested in an experimentalist who can develop large datasets in support of emerging artificial intelligence and machine learning driven advances in fluid dynamics
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learning class. Required Qualifications • Master’s degree in forest products or industrial engineering or related fields. • Demonstrated experience with pallet or packaging design and testing. • Experience
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well-being to begin Fall 2026 in Blacksburg, VA. This position is a nine-month, full-time appointment responsible for teaching, research, and service. There is an expectation to teach courses in
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identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees