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are united in our efforts to understand, explain and improve our world and the human condition. Description of the workplace The position will be placed in the Division for Computer Vision and Machine Learning
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is linked to the ELLIIT project New Machine-Learning Methods for High-Dimensional, Population-Scale Health Data , conducted in collaboration with Lund University. The project aims to develop and apply
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project has a strong focus on developing and applying machine learning and computational methods for protein design, in close integration with experimental enzymology and biocatalysis. The tasks include
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on the 25-qubit device. Project 2: Machine Learning for Quantum Transpilation This project aims to bridge the gap between idealized quantum circuits and physical hardware constraints. ML Architecture: Develop
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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vision, machine learning, deep learning, bioinformatics, advanced microscopy, cell biology, or RNA biology. Education in mathematical statistics. Experience in deep learning, computer vision, or neural
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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Doctoral student in Applied Mathematics with a focus on Computer Vision and Spatial AI (PA2026/1165)
are united in our efforts to understand, explain and improve our world and the human condition. Description of the workplace The position will be placed at the Division of Computer Vision and Machine Learning
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the subject of Machine Elements, where the research is primarily focused on tribology and its applications. The research group currently consists of about 40 researchers and doctoral students and is one
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the progression of ARDS in intensive care patients with sepsis. To enable this, we will develop information-theoretic machine learning methods to determine which protein interactions are driving disease progression