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on but is not limited to the following areas: (1) security analysis on the existing lightweight cryptography algorithms, (2) performance measurements for different algorithms on different platforms with
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algorithms and theories in domains, such as space and medicine. · Practical quantum error correction algorithm designs. · Fault-tolerant implementations of quantum algorithms in different quantum
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This Masters or PhD project aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and
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This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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imaging, based on absorption, provides good image contrast between high- and low-density materials, such as bones and soft tissue. However, it cannot distinguish subtle density differences between soft
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systems that are both scalable and explainable. This role bridges algorithmic research and systems implementation, offering opportunities to collaborate with leading academics and engineers on developing
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energy, and condensed matter applications. The scientists at BNL who are part of C2QA’s theory and algorithm thrust span these different domains in physics. We are thus particularly interested in
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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected