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Field
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. Responsibilities: Conduct research on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects
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dynamics out-of-equilibrium, and applications to quantum simulation, quantum optimisation, and related quantum algorithms. Depending on the candidate’s interests and expertise, the project may also involve
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development of explainable and interpretable classical and quantum deep learning algorithms - Design and development of hackathons and other collaborative methodologies to promote the control of artificial
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scientific leaders and researchers. For more details, please view https://www.ntu.edu.sg/spms . We are looking for a Research Assistant to support research in AI-enabled seismic data analysis. The role will
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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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these efforts, we aim to accelerate the translation of molecular and clinical insights into tools that improve patient outcomes. More information about the lab can be found here: https://www.nathlab.org As a
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(the final scope will be developed together with the candidate, and it is not necessary to cover all topics): Group fairness: developing and analysing algorithms and metrics for ensuring fair treatment
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top setup prototyping the translation of dynamic quantum algorithms such as double-bracket algorithmic cooling to applications in quantum sensing using NV centers. With whom? With Prof. Marek Gluza
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . Our team is seeking a highly motivated Research Assistant to contribute to cutting-edge research in embodied AI and
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. The tasks include battery cell characterization and modelling based on laboratory tests, and development of algorithms for estimating the charge level, health, and power capability which includes robustness