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, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3 , N2 O, CO2 , CH4 ) using satellite observations, atmospheric chemistry models, and physics-informed deep learning
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning
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https://www.timeshighereducation.com/unijobs/listing/406466/asari-postdoc-in-zo… Requirements Additional Information STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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or suspension eukaryotic cell lines). Designing, establishing protocols and conducting recombinant protein expression experiments using eukaryotic or prokaryotic systems (HEK293, CHO, E. coli, etc.). Applying and
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. • Contribute to interdisciplinary research projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep
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of recommendation, and full transcripts for academic transcripts for their previous studies. The preselected candidates will be contacted and interviewed. Salaries and working conditions. The Post-doc program is
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health? This topic is in line with one of the orientations of the Center for African Studies: health. The candidate will collaborate with the Medical School. He /She has to be able to teach in English
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geospatial modeling approaches to analyze urban environmental processes. • Apply machine learning and geospatial data analysis techniques to large Earth Observation datasets. • Produce high-quality
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closure in the final months leading to harvest, from January to September 2025, as well as post-harvest over the early developmental stages of the same clone of a Eucalyptus spp plantation subject to a