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machines by interpreting brain activity (e.g., EEG signals) in real time using energy-efficient neuromorphic hardware. You will work on the design and hardware realization of Spiking Neural Networks (SNNs
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challenges in developing future BCIs is to increase the number of brain signal channels while keeping the system very small and energy-efficient. This is very important for implantable or wearable systems
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transcripts and BSc/MSc diploma (in English) including official description of grading scale In the field “Please indicate which position(s) you would like to apply for”, please indicate which project you are
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to sediment transport and landscape evolution. Depending on your background and expertise, your research will focus on one or more of the following areas: Confocal microscopy of point defects and trapped
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available studies indicate that RL-MPC control strategies can result in significant savings in operational energy management for a building. However, the impact and scalability of these results for the large
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or remotely operated robotic systems, preferably in underwater or field robotics. The candidate should have strong competencies in control theory, sensor fusion, and signal processing, and demonstrate
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/official English translation). Completed TEK PhD application form for 5+3 applicants. Find the form at the Faculty website . Completed TEK PhD form for calculation grade point average. Find the form
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the scholarship for a period of two years. At that point, the terms of employment and payment will be according to the agreement between the Ministry of Finance and The Danish Confederation of Professional
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for intelligent brain-computer interfaces? We are offering a PhD position in analog/mixed-signal CMOS circuit design for EEG and wearable sensor interfaces, as part of a pioneering project focused on assistive
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to sediment transport and landscape evolution. Depending on your background and expertise, your research will focus on one or more of the following areas: Confocal microscopy of point defects and trapped