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at the Science for Life Laboratory in Stockholm, a centre for large-scale life-sciences. NRM has a close collaboration with nearby Stockholm University, which includes joint supervision of PhD students. NRM has
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distributed data processing approaches to address issues of incomplete or redundant multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will
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quantitative methods to analyse large-scale datasets and generate insights that influence policy globally. Key responsibilities include: Leading international research: Conducting high-quality studies with a
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 1 day ago
experience handling large genomic datasets, developing analysis workflows, and contributing to funding bids. Strong teamwork, supervision, and communication skills, along with a commitment to equity and
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) or with big, ambitious ideas that extend the lab’s focus. You will receive direct mentorship, generous support, and space to launch a scientific program you can carry with you into a future faculty role
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project to better understand how to build geophysical models of the subsurface which account for the uncertainty in current geophysical data, in order to assess the potential for sustainable energy
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isolated from the air to improve our understanding of biodiversity data capture and of ecological networks. The research fellow will primarily be based at the People and Nature Lab on the new UCL East campus
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Information Eligibility criteria The candidates should have experience in modeling large-scale networks comprising multiple neuronal populations, and demonstrate solid knowledge of analytical and numerical
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field. Have proven expertise in statistical modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in
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data from major facilities, including NASA’s Kepler, TESS, and JWST missions; ESA’s Gaia mission; and large-scale spectroscopic surveys such as LAMOST. Experience working with large, complex datasets is