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Field
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& Machine Learning not excluding other modeling frameworks) of safety critical socio-technical infrastructure systems. The candidate will be primarily responsible for writing and submitting refereed journal
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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methods to rigorously assess the safety and effectiveness of medications in real-world patient populations. Defining individualized treatment strategies: Leveraging traditional and causal machine learning
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, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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Qualifications PhD degree in computer science, data science, or related fields. Effective written and verbal communication skills Robust machine learning and natural language processing skills Proficient computer
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with