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Field
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is on analysing first-person descriptions of conscious experiences with the help of machine learning and large language models (LLMs) to identify, compare, and systematize different types of states of
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this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural
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references A copy of the doctoral degree certificate Where to apply Website https://lu.varbi.com/en/what:job/jobID:851159/type:job/where:39/apply:1 Requirements Research FieldBiological sciencesEducation
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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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postdocs at Chalmers, and collaborate with academic and industrial partners in Sweden and internationally. The role also offers opportunities for travel and engagement with external collaborators. Research
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letter in which you describe yourself, why you want to do a PhD and why you are suitable for this position. CV (max 2 pages) A certified copy of your master's degree and your course grades. Copies
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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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to either first-principles calculations or AI supported database management of high-throughput type calculations/simulations. Basic knowledge of density functional theory (DFT) is beneficial. Strong
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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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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