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Field
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electromagnetic device performance analysis>>>Embedded SystemsResearch focuses on:•Robotics, computer vision, and machine learning/deep learning•Wearable and implantable sensors, biosensors, and tele
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preprocessing IoMT network traffic datasets. Implement and evaluate machine learning algorithms (e.g., logistic regression, SVM, random forest) for intrusion detection. Develop prototype software tools (e.g
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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candidate will play a key role in designing and implementing innovative solutions at the intersection of sensor data collection, machine learning, and real-time decision-making. Specifically, the candidate
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. Applicants with a strong background in optimization (including machine learning), algorithmic game theory, decision theory, computational social choice, social welfare, or fairness optimization are welcome
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with a background in Language Technology, Machine Learning, or Language Typology who are interested in automatic classification and analysis of hundreds of languages to undertake a PhD project exploring
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identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision
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University of Montreal-CHU Sainte Justine Children's Hospital Research Center | Canada | 22 days ago
-Language Pathology, Neuroscience, Psychology, Cognitive Science, Biomedical Sciences, Biomedical Engineering, or related field.-Experience with machine learning (preferably RL) and applications in health
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health, and bioinformatics. You will apply advanced AI methods - from classical machine learning to large language models and agent-based AI - on large-scale healthcare datasets, including structured
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. Desirable Qualifications: Experience with cryogenic systems and low-temperature measurements. Knowledge of artificial intelligence (AI) and machine learning (ML) for inverse design of metamaterials. Previous