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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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machine-learning-driven multi-criteria decision analysis to rank and select optimal decarbonization pathways. Collaborate with industry and academic experts to ground-truth results. Dissemination Publish in
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. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and deep learning techniques to improve image processing and trait prediction. Analyze large
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oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development
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-driven modeling, simulation and optimization with strong and permanent interactions with experiments. Integrate artificial intelligence (i.e. machine, deep and reinforcement learning…) to optimize
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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research, oncology microbiomes, or environmental resistome surveillance. Familiarity with spatial metagenomics, single-cell microbiome analysis, or multi-omics data integration. Knowledge of machine learning