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foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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, machine learning, or data analytics. As a proficient programmer (ideally Python), you will be curiosity-led, with exceptional communication skills, and thrive in a highly interdisciplinary environment. You
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the Manchester BHF CRE: Geometric Deep Learning for Complex Manifolds: Novel deep learning theories, models and architectures to simulate interactions within non-Euclidean, patient-specific cardiovascular
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and in vivo models. The prospective candidate will have opportunities to work within a highly interactive, multidisciplinary team. Preferences will be given to candidates who Have some relevant research
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Professor / Associate Professor / Assistant Professor / Research Assistant in Quantitative Marketing
inference, or machine learning techniques; and (ii) are open-minded and committed to teaching excellence at both undergraduate and graduate levels. Applicants for the Research Assistant Professor position
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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provides an accessible, quality education through flexible learning and unparalleled internship opportunities. At UA Little Rock, we prepare our more than 8,900 students to be innovators and responsible
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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. The candidate is expected to have an overall interest in AI concepts and methods, in particular human-centred AI, and expertise in formal models and machine learning, as demonstrated by publications and other
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analysis and learning models applied to brain and physiological signals. The objectives of this fellowship are: 1. Survey of the state of the art in science and technology of artificial intelligence models