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trials, and treatment guidelines, and transform these into structured knowledge graphs encoding relationships among histotypes, biomarkers, therapies, and outcomes. Assess the accuracy, completeness, and
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expertises including GeoAI, geo-semantics, cognitive/behavioral geography, geographic information retrieval, social sensing, knowledge graphs, geocommunication, and geo-visualization. Our topical and cultural
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-dimensional biomedical datasets, including transcriptomics, proteomics, secretomics and other molecular data Apply pathway, network, graph-based and mechanistic modelling approaches to unravel adverse effects
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., PostgreSQL / SQL) Graph databases (e.g., Neo4j, Memgraph, or equivalent) Familiarity with cloud computing environments (e.g., Azure, AWS, or GovCloud equivalents), including containerized or scalable ML
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addressing research questions relevant to data science, biology, and agroecology the aim is to improve data flows and create knowledge graphs and future visions of landscapes. The tasks will encompass
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are seeking a highly motivated and interdisciplinary Research Fellow to
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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. Solid background in signal processing, statistics, or computational neuroscience, preferred. Outstanding presentation skills, preferred. Experience in graphing, statistical analysis and data management
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prepares graphs and charts for management use or presentations about departmental or divisional activities. • Coordinates and arranges meetings, conferences and committee meetings. • Makes complex travel
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whole-genome duplication across diverse plant systems (see https://www.yantlab.net/ ). The project is funded through a Formas grant aimed at restoring European ash (Fraxinus excelsior) populations