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, higher-order network methods and large-scale language-model training on a knowledge graph of scientific publications, patents and related innovation data. The appointed researcher will lead DII's
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Neurodevelopment. You will develop, implement, and apply computational pipelines to analyse large-scale genomic datasets generated through Perturb-seq and other functional genomics approaches. You will use state
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, Python or R Experience in manipulating and analyzing large observation data or model outputs Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes Strong communication skills
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consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ and remote sensing data from
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University of California, Los Angeles | Los Angeles, California | United States | about 22 hours ago
clinical data. A major focus of the position will be identifying genomic and immunologic features associated with variation in immune responses and clinically relevant phenotypes. A Ph.D. or equivalent
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on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute to the activities of the Machine Learning & Data
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/or oceanography; or experience working with numerical simulations, large data, scientific programming, and/or machine learning. Applicants are asked to send a CV, a brief statement of research
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(Large Language Models, Convolutional Neural Networks, Machine Learning) for analysis and classification of data.
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collection, and statistical analysis. Proficiency in statistical software (e.g., R, SAS, STATA) and data management techniques. Demonstrated experience in analyzing large epidemiological datasets. Excellent
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data storage, enabling the encoding of text, images, videos, and more into nucleotide sequences. While DNA data storage technologies are advancing rapidly, computation on large DNA-encoded databases