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(IoT) device ecosystem. Despite the technology’s potential, however, flexible electronics face numerous technology challenges. This PhD project aims to tackle one of the most critical technology
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the esophagus with relevance to eosinophilic esophagitis. The research project will involve patient-derived samples, including organoids and air-liquid interface cultures, and experimental esophagitis models
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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background in physics, biophysics, biological physics, or bioengineering. This PhD project will primarily focus on experimental research, which will include data analysis and there is scope for modelling
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post-zygotic aneuploidy arises in mammalian embryos and its consequences during early pregnancy. You will primarily work with equine embryos, which represent a valuable model for studying post-zygotic
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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research questions from several fields such as software and domain engineering, system modelling and analysis, application adaptability and integration, human computer interaction, security, and
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of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
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MSc/PhD Position at the Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
processing tools, and a 10X genomics platform for single-cell and bulk RNA sequencing. The place also offers numerous collaborative opportunities. More info: https://www.mun.ca/medicine/administrative
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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy