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find some of our publications here: https://i.giwebb.com/research/computational-biology/ Required knowledge A solid grounding in artificial intelligence and machine learning. Learn more about minimum
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, with the ability to write, edit, and proof publications for diverse audiences high-level computer skills, including proficiency with Canvas and/or other Learning Management Systems, Microsoft Office
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Multiple Continuing (Tenure-Track) Academic Positions, School of Electrical and Computer Engineering
-physical Systems Machine Learning and Artificial Intelligence Quantum Devices and Systems Robotics Security and Dependability Signal Processing Software Engineering All positions are full-time, continuing
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evaluation of curricula to facilitate and develop appropriate, effective, and innovative learning solutions. Proficiency in a range of computer skills, including multimedia design and production. Experience in
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a wide range of teaching, learning, and research activities that explore the intersection of media, design, and emerging technologies. Our work spans diverse tools and platforms - including video and
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Machine learning, dynamical systems theory, control theory, signal processing, network theory, neuroscience are all relevant and a student should have strong knowledge in at least one of these and a
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, and subject matter experts to identify training needs and develop scalable solutions (e-learning, workshops, coaching) Provide expert advice to senior leaders on change and training strategies
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machines and drives Electrification Key Responsibilities As a Lecturer (Level B), you will: Deliver high-quality teaching across undergraduate and postgraduate programs in electrical and electronic
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. An example of duties you will perform include: Deliver engaging training and assessment in mechanical engineering and fabrication including CNC machining, laser cutting and CAD/CAM Develop and update learning
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reinforcement learning. In International conference on machine learning (pp. 2107-2128). PMLR. - Péron, M., Becker, K., Bartlett, P., & Chades, I. (2017, February). Fast-tracking stationary MOMDPs for adaptive