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to advance the project. The successful candidate will have extensive experience developing models and algorithms for analysis of noisy biological data, a deep background in analysis of stochastic processes
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data and IT infrastructure. Combination of different algorithms to test multimodal predictive modelling. Compilation and presentation of data orally at seminars and conferences, as well as independent
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale
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Engineering, Neuroscience, or related fields. Tasks to be performed: Collection of experimental data in humans, development of neural decoding algorithms from bioelectrical signals, and development of a
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tomography and local adaptive reconstruction to overcome these challenges. Your tasks develop physics-informed, self-supervised learning approaches for phase retrieval implement reconstruction algorithms
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datasets, algorithms, and tools for specific AI applications Integrate AI capabilities into existing software systems and applications Collaboration and Documentation (20%) Collaborate with cross-functional
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software Analyze software for new, moderately complex systems and algorithms Perform data analysis, test and debug software Develop, implement and execute plans and tests Design and apply basic data sources
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efficient exploration of this multidimensional space. Working with the colleagues at the University of Liverpool, you will also help develop process¿optimisation algorithms toward autonomous operation and
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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative
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the unique nature of clinical medical data with low disease prevalence and difficulty obtaining ground truth data. Major regulatory science gaps include lack of methods for AI algorithm training with limited