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in motor control by computational modelling and non-invasive brain stimulation. The focus of this project will be on advanced versions of transcranial alternating current stimulation (tACS), targeting
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. Full-time / part-time full-time Programme duration 8 semesters Beginning Winter semester Application deadline Middle of January for the following winter semester starting on 1 October Please check our
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become a KTP Associate? The KTP programme is designed to accelerate your career, expand your skills, and grow your professional network. You will have the opportunity to: Work on a high-profile, funded
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(Centre for Doctoral Training in Modelling of Heterogeneous Systems) at the University of Warwick is an innovative, interdisciplinary fully funded PhD programme that brings together science, engineering
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 11 hours ago
for the position of research assistant professor with a significant outreach component. Expectations of the position will be to develop an externally supported research program in agroforestry and contribute
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with our industrial and UK university partners, a novel research programme will be implemented based on modelling, control and optimisation of an integrated power and transportation networks. Key
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researcher? Do you want to be trained in cutting-edge experimental and computational techniques to understand how clot contraction influences thrombus stability and embolism risk? Are you excited to be part of
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researcher? Do you want to be trained in cutting-edge experimental and computational techniques to understand how clot contraction influences thrombus stability and embolism risk? Are you excited to be part of
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of real-time adaptive 3D inspection, dynamically adjusting its measurement strategy based on data quality as well as environmental and scene cues. Positioned at the intersection of robotics, computer vision
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap