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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where
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, traceable industrial packaging that can be shared between manufacturers, we are recruiting a deep learning Post-doctoral researcher in our Mathematical and Electrical Engineering (MEE) Department. Using
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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This project aims to advance representation learning in cybersecurity by developing deep learning architectures capable of extracting high-level, structured, and semantically meaningful representations from
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employment. Starting date: 09.04.2026 Job description:PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 987 - 2026/WP 1 Work location: Hamburg Application deadline
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 2026
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identification framework. The role involves developing a species-agnostic computational pipeline that integrates cutting-edge deep learning methods - such as AlphaFold2 and FoldSeek -to enhance protein function
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computing, and synchrotron-radiation experiments. The goal is to explore physics-informed self-supervised learning approaches (e.g., deep image priors, GANs) and iterative methods, combined with multi-scale
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project at the forefront of microscopy and bioimage analysis (https://www.msca-agile.eu/ ) and contribute to OMERO integration and optimization of Biom3d, a cutting-edge deep-learning framework for 3D image