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recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning, behavior, and
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collaborative and international projects Experience/knowledge in HIL systems Hands-on lab experience and/or interest Knowledge on Machine Learning, or other AI techniques Team Worker Initiative in Research and
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and Wednesdays. Teaching associates may teach one or more courses depending on their preferences and the program’s needs. The Spadoni College of Education and Social Sciences is CAEP accredited
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Ability to lead and work in teams Essential Application/Interview Experience and capability in blast computational simulations using codes such as Viper:: Blast, machine learning, and/or LS Dyna Desirable
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» Autonomic computing Engineering » Maritime engineering Technology » Computer technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 25 Apr 2026 - 23:59 (Europe
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 24 days ago
-throughput screening; - Cell painting assays and high-content image-based analysis (e.g., CellProfiler, Harmony); - Machine learning models for antimicrobial activity prediction (e.g., Weka); - Strong
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Sessional Lecturer - PPG2012H-S-Topics: Applied AI Systems & Governance: Technology, Policy & Practi
-world policy applications to equip students with the knowledge and tools needed to engage with AI at both strategic and operational levels. Students will learn how modern machine learning models work
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Project description Electromagnetic (EM) sensing is emerging as a powerful enabling technology for modern high-value manufacturing. Advances in computing power and machine learning now allow us to
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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
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Job description: DESY The CMS Quantum Computing group develops generative machine learning models for detector simulations, specifically the simulation of showers in calorimeters: Proof-of-principle