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We delivered an invited talk titled: "High-dimensional computational modeling of an octopus arm” as part of the excellent workshop “Embodied Exploration Through Muscular Hydrostats”, organised by Prof Cecilia Laschi (NUS, Singapore) and Dr Lucia Beccai (IIT, Italy).
If you are interest to know more about this research segment, take a look at these two papers:
- Nature Article
- AnnualReviews Article
We extend a warm welcome to Deeksha Varshney, who joins MathEXLab to work on natural language processing for weather and climate applications.
CDE, in collaboration with NVIDIA, recently brought together experts from industry and academia for a day-long workshop at NUS to explore the potential intersections between artificial intelligence, climate science, and quantum computing.
We are participating to SIAM Computational Science and Engineering (SIAM CSE) conference in Amsterdam from 26th Feb to 3rd March 2023. Join our talks:
• Spectral/hp Element Methods for Industry: Some Challenges on the Way
• Neural-Network Interpretability for Time Series Classification Task
• Dynamics of Weather and Climate on Reduced Manifolds
• Stochastic Chaos and Predictability in Laboratory Earthquakes
We are hiring 1 PhD student in explainable machine learning for sequential data and time series. The ideal candidate should be skilled in coding, and software development, and be highly proficient in Python, Tensorflow or Pytorch, large-scale sequential-data analysis (including time series), neural networks, explainable AI. The project is in collaboration with several partner institutions, including University of Geneva (Switzerland), Scuola Superiore Sant'Anna (Italy), and University of Cambridge (United Kingdom), the latter starting from 2023. The primary objective of the project is to develop novel tool to improve the interpretability of the results provided by neural networks, when applied to time series and sequential data(e.g., text).
We are hiring 1 Postdoctoral Fellow in dynamical system theory and machine learning for extreme weather forecasting applications. The ideal candidate should be skilled in coding, and software development, and be highly proficient in Python, large-scale spatio-temporal data analysis (ideally the ERA5 and other reanalysis datasets), dynamical system theory, reduced order modelling (including POD, SPOD and Autoencoders), and neural networks. The project is in collaboration with several partner institutions: ECMWF , Argonne National Laboratory (USA), CNRS (France), and University of Cambridge (United Kingdom), the latter starting from 2023. The primary objective of the project is to provide a fast computational framework for extended-range extreme weather forecasts, as well as quantify damage and develop mitigation strategies for extreme weather events.
We are running a seminar series across different disciplines for the Fall and Winter of 2022/2023 in the Department of Mechanical Engineering at NUS. The aim is to bring leading experts with a multidisciplinary background covering topics that include (but are not limited to):
• Computational science and engineering.
• Materials and manufacturing.Robotics and intelligent machines.
• Energy and sustainability.
• Thermo-Fluid dynamics.
• Structural design and applied mechanics.
If you are interested in giving a talk, please contact us
