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develop independent research activities on the air pollution modelling. Candidate Profile: The Center is looking for a Post-doc to work at the interface between Air Quality modeling and machine learning
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency
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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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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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analyzing urban data (traffic, energy consumption, environment). Strong skills in integrating IoT devices into complex digital systems. Advanced expertise in machine learning and artificial intelligence
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city models focused on health and environmental infrastructures. Advanced Data Analysis: Advanced skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban
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) combined with machine learning and chemometrics. Key Responsibilities: The Postdoctoral Researcher is primarily intended to support leaf spectroscopy research but will also be involved in other research
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skills/programming/modeling would be a plus. Candidate Criteria Ph.D. in Chemical engineering or a related field. Extensive experience in unit operations and chemical processes. Ability to design and