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devices—such as wearable sensors, assistive robotics, or implantable systems—where real-time performance, energy efficiency, and reliability are critical. Unlike traditional NAS approaches that are hardware
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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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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, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted to complex 3D underground geometries. The research will be conducted in close
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create predictions for how foragers should vary in their stay-or-leave decisions for different types of decision algorithm. You will then get to test those predictions using data from humans and rodents
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intelligence algorithms for online action recognition and user monitoring using smart eyewear. The research will address the problem by leveraging multiple modalities, integrating different sensing sources
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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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: Conduct descriptive and inferential analyses to answer policy-relevant research questions. Apply econometric and causal-inference methods (e.g., difference-in-differences, instrumental variables, propensity
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: Pattern and Image Analysis – Lx Work Objectives: - Evaluate and compare different causality discovery algorithms under controlled and realistic conditions. - Develop a robust and interpretable causal model
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methods (e.g., difference-in-differences, instrumental variables, propensity-score methods) and selected machine-learning algorithms. Write reproducible code in Stata, R, SAS, or Python; summarize and