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/ Electronics Engineering, Computer Engineering, Computer Science, Robotics, or a closely related discipline, with foundational knowledge in signal processing and machine learning. Working knowledge of computer
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us as soon as possible. Keywords: AR/XR, human-computer interaction, information visualization, data storytelling, geospatial data, graphical perception, cognitive psychology, social cognition
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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mechanisms of learning, memory formation, perception, and behavior. Researchers with a proven track record in neuroengineering and related fields — including neuro-inspired hardware, brain-machine interfaces
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uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Physics-guided learning for machine control Description: Robust machine control assumes modeling of robot-environment
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Tiny Machine Learning (TinyML). The role will focus on the design and development of battery‑less, ultra‑low‑power IoT systems capable of executing secure TinyML‑based visual perception algorithms
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, Electrical Engineering, or a related field. Experience in robotic perception, control, or learning-based methods. Proficiency in Python and C++ for AI and robotics applications. Familiarity with robotic
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at KTH Royal Institute of Technology is seeking a highly motivated doctoral student with a strong background and interest in spatial AI, navigation, sensor fusion, optimal estimation, and machine learning
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they are mainly based on predetermined rules of behavior chosen by the designer. More recently, methods derived from machine learning provided impressive results. However most are datadriven, meaning
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manipulation tasks. We are seeking candidates with a strong background in robotics and machine learning, and demonstrated experience in two or more of the following areas: deep learning, reinforcement learning