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computer programming tools such as Matlab and/or Python. Knowledge of statistics and mathematical modeling. Experience in large spatial data processing, analysis, and interpretation. Experience in running
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
-dimensionality and complexity of metabolomics data, requiring advanced AI/ML techniques for robust analysis and interpretation. Integration of multi-omics data (genomics, transcriptomics, proteomics, and
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of thermal energy storage systems, enabling the modeling and analysis of heat transfer, fluid flow, and thermodynamic behavior within the storage system to optimize design and performance. Demonstrated
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language models for bacterial genome analysis. Train and evaluate models on large-scale bacterial genomic datasets. Apply the developed models to address specific research questions, such as gene function
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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for large scale on-farm experiments with the components of biochemical analysis of precursors of soil carbon sequestration and nutrient cycling Analyzing and developing models for quantifying carbon
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of education and development. This unique nascent university, with its state-ofthe-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high
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). Analyzing and characterizing purified proteins using SDS-PAGE, Western blot, ELISA, UV spectroscopy, BCA/Bradford assays, etc. Actively contributing to critical data analysis, protocol optimization, and the
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Polytechnic University aspires to leave its mark nationally, continentally, and globally. About ACER CoE: The centre has been recently created to address enduring process challenges in Chemistry and Engineering
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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