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science research. You will have experience of handling complex survey data at scale. The role holder will support this research using publicly available government and other data (which include NFHS, DHS
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: Archeometallurgical analyses SEM-EDS microscopy and chemical composition analysis of iron artifacts, slags, and ores GIS spatial analysis, statistical analysis, and network analysis of iron production Other
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. Develops understanding of advanced theoretical concepts. 5. Work is varied and somewhat difficult. Originality and ingenuity is required. 6. Receives direct supervision referring complex situation to higher
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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multiple lines of treatment for more than 1000 patients have been collected. Multiple samples derived datasets have and are being generated, including genomic DNA analysis, serum proteomics, peripheral blood
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insight provided by the experimenter, using methods such as Proper Orthogonal Decomposition (POD) [8], spectral analysis, or Dynamic Mode Decomposition (DMD) [9]—which are currently being implemented in
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econometric models. 2. Analytical skills: • Ability to analyze and interpret complex data, identify trends and formulate recommendations based on analysis. 3. Communication skills: • Excellent written and
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econometric models. 2. Analytical skills: • Ability to analyze and interpret complex data, identify trends and formulate recommendations based on analysis. 3. Communication skills: • Excellent written and
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learning analysis of biomedical data and bioscientific programming for projects on neurological diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical
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quantification and mapping of the high-dimensional correlation structure across multiple traits, quantification of trait variation that arises from new mutations, development and applications of analysis methods