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., computational learning theory, expressiveness of transformers and other models, xAI, formal methods) or equivalent; willingness to conduct research on artificial intelligence/machine learning, combined with
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, scalability, and effective performance across university use cases. Develops, trains, and fine-tunes machine learning models for a variety of university applications. Conducts experiments to evaluate model
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grant that focuses on modeling spatio-temporal medical image analysis with a particular focus on learning from limited labelled data. Here you will be a part of the UiT Machine Learning Group and will
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representativeness - Knowledge of software engineering for AI applications - Knowledge of mathematical probability and statistics and optimization methods - Knowledge of machine learning including
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and supervise PhD students. The department has developed several courses within data science, e.g., Bayesian methods, Advanced Machine Learning, Deep Learning and AI methods. You are expected to teach
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Neutral Infrastructure (dfCO2), this role contributes to Program 4: Machine Learning for Carbon Performance (https://dfco2.org.au/program_4 ) that aims to advance the next‑generation AI methods to model
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, and in-depth data analysis? We're looking for a fast-learning individual with strong transferable research skills to join our Digital Machining team as a Project Engineer In this role, you'll be
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analysis (e.g. regression, correlation, multilevel modelling, structural equation modelling), and advanced analyses (machine learning). Attends trainings and skill development workshops as necessary
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of the Alhambra and the Generalife. Project 2 — Machine learning for energetic-particle transport in thunderstorms This project explores machine-learning (ML) techniques to accelerate the numerical simula- tion
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applications, including development and validation of machine-learning and statistical models for disease prediction, prognosis, and therapeutic response. Proficient in R, SAS, and other bioinformatic tools