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Field
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attracted multidisciplinary research teams composed of food engineers, agronomists, biochemists, biologists, … etc. Description of the position: Provide research support in thematic areas related
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strong emphasis on real-world impact and societal relevance. This position is anchored within the Signal Processing and Machine Learning section at ECE and will be mentored by Assistant Professor Muhammad
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large facility with state-of-the-art equipment and software to simultaneously collect choice process data including eye tracking, facial expression analysis to assess human emotions, neural signals
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, statistical signal processing, optimization theory, machine learning and artificial intelligence. The candidate is expected to actively participate in experimental work focused on building datasets of channel
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-doctoral position will synthesize various chemical probes that are designed to have selectivity or unique signaling properties at various GPCR targets. Projects will involve chemical synthesis of analog
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modern world history. Our PhD students and early career researchers are appointed to many of the top positions in their field. We are currently (2024) the 6th best History department in the UK, and 23rd in
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to developing software solutions for data collection, signal processing (e.g., drift correction) and visualization. Ultimately, the goal is to demonstrate the prototype at the seabed. The candidate is expected
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& Responsibilities Analyze neural data using advanced signal processing techniques. Write up manuscripts and generate figures for manuscripts related to the publication Contribute (writing and figures) to grant
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challenges in rural areas. It is specifically targeted towards hard-to-reach populations, and aims to incorporate novel methodological advances to improve our understanding of these processes and point towards
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closely with senior members of the research group. The term of the positions is typically two years, with the possibility to renew for the 3rd year, contingent on the project process and availability