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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January
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English. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high-performance and cloud computing; (iii
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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Proficiency in ionic liquid handling and techniques Excellent written and verbal communication skills Ability to work independently and as part of a team Experience with machine learning in engineering is
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inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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: MSc degree completed. Additional optional skills and qualifications: Previous research experience, particularly in the fields of Internet of Things security and machine learning models applied
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active in the late 19th and early 20th centuries. The Fellow works closely and collaboratively with archivists in the department to learn and apply archival standards and best practices while housing
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axes: AI-driven territorial diagnostics and foresight, integrating multi-source satellite data with machine learning and spatial modeling Climate–water–energy–agriculture interactions, with applications