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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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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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or explainable AI or safety). Experience in machine learning, causal inference, image processing, human-robot interaction, or large language models. Experience in analyzing multimodal data (e.g., text, sensor
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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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many research synergies coming together on the main thread of machine learning and Artificial Intelligence (AI). The successful candidate will join the newly established research group AI in
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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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This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research effort at the intersection of machine learning and materials science. The project brings together PhD
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advanced biostatistics/machine learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a “user fee-based” support model. The projects will differ in
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introduces new and underexplored vulnerabilities to network-based threats. The goal of this research is to uncover such threats, evaluate their impact on training performance and model integrity, and develop
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative