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Field
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position will be placed in the ISOVIS group (http://cs.lnu.se/isovis/ ), led by Professor Andreas Kerren. The research group mainly focuses on the explorative analysis and visualization of typically large
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analysis and visualization of typically large and complex information spaces, for example in biochemistry, humanities, or software engineering. Our vision is to attack the big data challenge by a combination
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oceanography with demonstrated strengths in large data analysis, multivariate statics, and acoustic theory, and optical theory. The position will be primarily located in the Lewes Campus of the University
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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. Organizational Status Reports to the Principal Investigator. Work Performed Conducting fMRI data preprocessing and analysis, the work is routine in nature or performed according to established protocols Creating a
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; data collection and analysis. The various tasks must be able to be performed independently: Stereotaxic surgery in mice for AAV-based sensors injection and implantation of intracerebral recording devices
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big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that captures neural activity and intelligent behavior at unprecedented
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Science, Statistical Physics, or any other related field. The skills that we are looking for include: Statistical analysis & causal inference Data management, collection & visualization Social network analysis & graph
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and creation of study designs and oversee data collection. Essential Functions: 1.) Data Analysis and Dissemination of Results The Postdoctoral Researcher will analyze data collected from on-going
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Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and analysis of large-scale 2D/3D scientific data