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Field
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intelligence. You have worked with tools, techniques, algorithms, software, and programming languages for deep learning, reinforcement learning, statistics, sensors and sensor fusion, planning, computer vision
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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise
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on the development of machine learning algorithms, particularly transfer and adaptive learning, for multimodal wearable biosensing and its translation to rehabilitation and digital health applications. It is co
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resolved single photon technologies and optical fibre sensors to tackle clinical/biomedical challenges and other real world applications. Our work is primarily experimental, developing measurement systems
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. This may include writing and testing software in high level languages; writing and testing logic in hardware description languages; developing and testing signal processing algorithms from concept
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algorithm level languages such as Matlab/Simulink, Python or similar * Demonstrated, successful experience developing software to communicate and exchange information (e.g. commands, status, data) with
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science. If you’re passionate about building real robots that make a measurable difference in the world, we’d love to hear from you. Your primary responsibilities are: Developing algorithms for perception
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control algorithms (such as adaptive control or model predictive control) to reliably maneuver Army projectiles to the target despite limited state information, control authority, and changing flight
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in the live organism to study e.g. immune responses or cancer development. Moreover, using molecular sensors we also aim to read out cellular functionality in vivo e.g. metabolic reprogramming in
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. The project is supervised by Associate Professor Ulysse Côté-Allard at the Department of Technology Systems, University of Oslo, whose research focuses on the development of machine learning algorithms