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Field
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estimation over dense tropical rainforest remains challenging. This 1-yr project attempts to improve forest biomass estimation through exploring the applications of machine learning techniques in AGB
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brain disorders such autism spectrum disorder and bipolar disorder, by utilizing recent advances in genetics and genomics, and through collaborations with groups using machine learning. We are developing
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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and remote sensing imagery for ecosystem monitoring. Develop machine/deep learning-based workflows to interpret ecosystem disturbance. Synthesize model simulations and multi-source observations
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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power systems, network analysis and power flow; - Experience or academic background in machine learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo
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, that can be realised using cloud compute infrastructure and other novel deployment architectures. Given our team's existing research skillset in novel machine learning approaches, we are recruiting a
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at NTU are looking for a Research Fellow (RF) to carry out in research in probabilistic machine learning, causal discovery and GenAI, by exploring cutting-edge approaches such as causal representation
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discrete choice modelling, behavioural data science or machine learning? Are you interested in developing the next generation of AI tools that accelerate scientific discovery while maintaining behavioural
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skills (Python, R, or similar) and experience building or maintaining analysis pipelines Experience with RNA-seq, single-cell genomics, and/or proteomics data Familiarity with machine learning or LLM-based