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is expected to soon be able to diagnose diseases occurring outside the retina. OCT images can be aberrated by the eye itself and imperfect optical design. Ocular imaging with adaptive optics promises
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tackles fundamental challenges in multimodal representation learning by developing novel approaches to align distinct embedding spaces from speech and sign language modalities. Sign languages encode
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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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of machine learning frameworks Your research will include using models and codes to investigate the optimized design, integration, and intelligent operation of thermal energy storage systems in industrial
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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(SELECTED TASKS) From requirements analysis to conceptual design and final creation and validation of Runtime Multimodal Multiplayer Virtual Learning Environment (RMM-VLE) based on DTCS and real-time IoT
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Vapour fed electrolysers can generate green hydrogen even from impure water sources. However, water vapour itself contains gaseous impurities. During this 3-year PhD, we will design and characterise
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters
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characterization and fingerprinting. design and execute experiments to trap single proteins. perform in-depth data analysis and pattern recognition to extract protein properties from electrical current reads