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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 16 days ago
the department and teach courses if they choose to do so. Education and Experience: Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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schools, doctoral supervision, and software outputs central to the Centre’s mission. About You You will have, or be close to completion of, a PhD/DPhil in Statistics, Machine Learning, Data Science, or a
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techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological