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foundational and applied topics in computer vision and machine learning, with particular strengths in inverse problems, generative models, and geometric deep learning. We work across diverse application areas
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] Subject Areas: Machine Learning Complex Systems Computational Science and Engineering / AI/ Machine Learning , Artificial Intelligence , Data Sciences , Machine Learning Theoretical Physics / Statistical
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description The postdoctoral project is focused on development and the exploitation of machine learning tools to accelerate the analysis of microtomography data at the MAXIV synchrotron facility. MAXIV
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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postdoctoral position in data analysis, where you will apply machine learning techniques to understand how resistance genes spread and to help detect infections caused by resistant bacteria. The position is part
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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of mathematical areas. The position will be placed at the Department of Computer Vision and Machine Learning (CVML) at the Mathematics Centre (https://maths.lu.se/). Mathematics Centre is a department
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The postdoc fellow will conduct research in the intersection of AI/Machine Learning and Software Technology. The advertised position will be placed in the DISTA research group (https://lnu.se/en/dista
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changes and established markers for Alzheimer's disease. The project may also include machine learning methods to estimate individuals' biological age. The project is based on existing data from a prominent