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with neuroimaging and neural signal processing tools, including fMRI, structural MRI, diffusion MRI, EEG, or related modalities. Strong publication record in AI, machine learning, computational
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equations, numerical analysis, mathematicalmodels in biomedicine, biostatistics, machine learning, statistical learning, multivariate statistics, time series,mathematical modelling, generalized linear models
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-generation permanent magnets using a powerful combination of high-throughput computation, machine learning, rapid synthesis and efficient characterization. This position requires expertise in synthesizing and
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at the time of accepting the position (a net period of time, which does not include parental leave, military service etc.) good skills in spoken and written English prior experience and knowledge in magnetic
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for a short-term 2.5-month postdoctoral position (full-time) focused on developing and validating machine learning models that predict soil health and crop performance. The position will exploit datasets
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processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude for team work and excellent communication skills in spoken and written
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, and computer vision. In particular, it focuses on the measurement of eye-gaze as a key window into cognition, integrating experimental, comparative, and computational approaches. We aim to move beyond
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and authorities interested in improving civic participation. The research in this postdoctoral position focuses on text and image feature learning for news ecosystems, analysing the complex
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Devices (including applications to Health), Electrical Engineering Systems (including Communications, Information Theory, Machine Learning), and Computer Engineering (including Embedded Systems, Security
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of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model