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, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural networks (CNNs), which identify local
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. Work duties The main task is to conduct research. Teaching may also be included in the duties. Work in this field includes the design, modelling, realization, and characterization of nanophotonic
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directions include: Quantitative genetics and phylogenetics: incorporating developmental constraints into evolutionary models and exploring how they shape patterns of variation. Modeling development from data
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compromising the therapeutic efficacy of radiation. This doctoral project aims to develop and validate predictive models for estimating the radiation dose delivered to circulating blood. These models can
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large language models (LLMs)—that is, the inability of a model to effectively process or understand visual information. This work involves integrating visual encoders with language models to create
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well as to reduce other negative environmental impacts, are central to the work. The work will build on the extensive knowledge and model structure developed within phase 1 of the research program Mistra Food Futures
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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knowledge and model structure developed within phase 1 of the research program Mistra Food Futures (mistrafoodfutures.se) and include extensive collaboration with various researchers and external
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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models, and other ML methods for analyzing and discovering patterns in probability distributions in