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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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, particularly in areas such as machine learning theory, probability theory, optimization, and linear algebra. Experience with random matrix theory is a plus. Programming experience in Python. Familiarity with
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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microstructure in materials. A distinctive element of the project is its data-driven approach. Together with colleagues at Saxion University of Applied Sciences, you will contribute to the development of machine
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planning and decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch
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studying how wireless sensing and AI interact in real systems. The work will be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with
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the start of the position Have practical experience in machine learning with Python, including training neural networks in PyTorch or a similar framework Have a solid background in signals and systems as
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are looking for candidates interested in developing new machine learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will
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skills, plus good presentation and writing skills in English, are required. Previous research experience in contributing to a collaborative interdisciplinary research environment is highly desirable but
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 23 hours ago
on any desired physical quantum computer. They have also shown themselves highly useful for optimisation as certain kinds simplifications are easier to perform in one model than another. Yet while