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data, thermophysical data and modelling approaches Knowledge/interest on data-driven approaches, i.e. machine learning Experience and knowledge in sorbent-based CO2 capture Experience of interaction
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AI systems and interpretable machine learning, System integration implementation, Test environment configuration, Validation and stress testing, Deployment and configuration in test environments
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comprehensive knowledge graph for SSH and integrating applications based on artificial intelligence and large language models, GRAPHIA will convert heterogeneous SSH data into more interoperable and machine
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and machine learning based modelling. Publish research data, prepare project progress reports. Assist in the preparation of grant proposals. Job Requirements: At least a Bachelor's degree from a
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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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composites for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization
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aspects of research, conducting field research and development of physics guided machine learning models. The primary project focuses for this Part Time Research position deepening the understanding
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 13 days ago
clinicians and healthcare partners. Main thematic axes AI-based predictive modeling in healthcare Risk prediction, outcome forecasting, and early warning systems Time-series and multimodal deep learning
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models. The role also requires significant experience in classical machine learning methods such as decision trees, gradient boosting machines, and both shallow and deep learning networks. A demonstrated