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Field
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). Information Key Responsibilities: Develop a generalizable and explainable (gray-box) model for adaptive patient monitoring. Utilize a mixed approach combining real and synthetic data for algorithm development
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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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candidate will develop and conduct empirical research examining human interaction with algorithmic decision-support systems in clinical contexts. The precise focus of the project will be refined
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the Unconventional Communications and Computing Laboratory (UC2), led by Dr Michael T. Barros, which develops modelling and algorithmic methods for networked communication and computation under real-world constraints
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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. Expected Work: The selected candidate will conduct research at the intersection of wireless communications, signal processing, and control theory, aiming to develop cutting-edge algorithms for joint
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lead to a PhD position at SPMS in NTU Singapore (based on performance). Key Responsibilities: Contribute to the development of algorithms Develop codebase in Python and perform large-scale simulations
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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Requisition Id 15854 Overview: We are seeking a research professional with fundamental knowledge in artificial intelligence (AI) who will focus on developing and applying AI algorithms to signal
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into learning architectures. Collaborating with experimental research groups, enabling real-world validation and application of newly developed algorithms.