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Field
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work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through
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processes that could be realised in neuromorphic hardware. The research will combine theoretical derivation and simulation-based validation, using mathematical modelling, algorithmic experimentation, and
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modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning for biomedical data will be advantageous. The successful candidate will contribute to high-impact publications
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including nervous system) Experience with data analyses using chronobiological algorithms (e.g. RAIN, Metacycle) High level of both spoken and written English (official certificate not required) High interest
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develop a feedback interface integrating human natural sensory and robot’s artificial information. This will encompass mechatronics design, system integration, robotic control, computer vision algorithms
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experience with implementing machine learning algorithms. Experience or interest in innovative control systems engineering including IEC 61499 would be an advantage. The candidate will be immersed in a
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use deterministic algorithms based on time-frame and frequency of code occurrence to generate a large number of binary features. This project will compare that type of approach with NLP-based feature
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Refine the pressure-sensing system, calibration and control algorithms Contribute to the durability testing and validation to meet ICU clinical standards Collaborate with a multidisciplinary team of
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for this position are: (1) Software Engineering (2) Computer Architecture and Operating System (3) Programming and Algorithms. Additionally, knowledge of Artificial Intelligence and Machine Learning and Data
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like you? Then join our accomplished team! Your personal sphere of influence: The research group “Foundations of Cryptography” within the research group “Theory and Applications of Algorithms