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differential problems. 2) Development of adaptive mesh generation algorithms for distributed order fractional differential equations. 3) Analysis of the stability and convergence properties of the developed
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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frameworks (e.g. PyTorch). Experience analyzing single-cell, bulk sequencing, or other biological data. Experience in algorithms and good software development practices. Good communication skills. About the
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by working to develop novel algorithms on finite element method, isogeometric analysis, geometric modeling, machine learning and digital twins to study various applications such as computational
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms at the intersection of robot learning, control, and AI
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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methods to improve the deployment, adaptation capabilities and safety of robots and critical infrastructures. The developed algorithms will be evaluated on legged robots, wheel-based robots and under