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values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker
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) to monitor and improve public health. You will contribute to modeling smart cities with a focus on health and designing decision-support tools for sustainable and inclusive urban environments. Key
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on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities
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Position Description: We are seeking a postdoctoral researcher specializing in urban modeling and cost analysis to develop an innovative model to estimate the direct and indirect costs
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
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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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) 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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to environmental stress. The successful candidate will work at the interface of molecular biology, physiology, and omics technologies to dissect regulatory networks in model and/or crop plants. Key Responsibilities
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tools, data science techniques, and spatial analysis to leverage big data from cities and model their evolution. Main Tasks and Responsibilities: Collect, clean, and structure large volumes of urban
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operate in research environments oriented toward sustainability and impact. The selected researchers will contribute to both experimental and modeling activities involving sorption and condensation