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research environment focusing on integrating multi-source data and developing novel algorithms to address the challenges posed by global environmental change. You will focus on integrating experiments, field
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information from ‘deep tissue phenotyping’ datasets. The successful applicant will have significant experience working with machine learning algorithms. They will have strong Python programming skills and
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the use of synthetic data in precision medicine research and applications through development of AI algorithms, tools and other processes to allow for the enrichment of clinical data sets Providing training
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, parameter estimation algorithms, and applications of sensing such as localization. Extreme MIMO: beamforming architectures and techniques for massive ultrawideband antenna arrays Gearbox PHY concept: Flexible
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algorithm Maintain a lab notebook of raw data and organize data for analysis by senior lab members Keep detailed records Qualifications Bachelor’s degree in Biology, Neuroscience, Psychology, Biology
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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of multi-omics data sets generated with innovative high-throughput technologies used in Research Sections I and II (e.g. sensory, metabolome, proteome, and transcriptome data) by using efficient algorithms
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of the surgical procedure (direct filming and arthroscopic video feed), and a device for recording heart rate. In the second phase, the student will propose signal processing and data fusion algorithms to reconcile and
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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimising PIC algorithms for modern