960 machine-learning "https:" "https:" "CMU Portugal Program FCT" positions in Sweden
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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning for batteries, with
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mathematics Appl Deadline: (posted 2025/12/11, listed until 2026/01/16) Position Description: Apply Position Description Postdoc in Algebra-Geometric Foundations of Deep Learning or Computer Vision KTH Royal
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Computing Science at Umeå University is looking for a doctoral student in machine learning
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description A central challenge in machine learning is ensuring
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at the intersection of artificial intelligence, robotics, machine learning, and human-robot interaction. Project description The focus of the project is machine learning and specifically the development of novel neuro
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doctoral degree (PhD. in Computer Science). Start date: Spring 2026. Project Description This PhD project explores the foundations of tractable probabilistic models, i.e. machine learning models that support