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Field
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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. To perform any other duties related to the research program. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical
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requirements PhD in Physics, Applied Mathematics, Computational Science, or a related field Strong background in machine learning, particularly in the development and application of neural networks
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, CPRD) or hospital electronic health records Experience with data linkage and working with routine healthcare data Experience with machine learning or AI applications in healthcare settings Advisory
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Responsibilities: Conduct research in Systems Biology to study cell signaling Perform large-scale signaling analysis or protein-interactome analysis Utilize machine learning-based biological data mining and analysis
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for spatiotemporal data (e.g., CNNs, LSTMs, Transformers, or Graph Neural Networks). Hybrid modeling: Experience with physics-informed machine learning or the integration of ML with data assimilation/multivariate
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well as the Horizon Europe project High-Performance Language Technologies (HPLT). The group represents extensive experience and expertise in web-scale data curation, development of large language models (LLMs), and in
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sociology. Strong quantitative skills and experience with large-scale data analysis required. Computer Science/HCI: PhD in Computer Science, Human-Computer Interaction, Information Science, or related
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consisting of senior researchers and 4 different PhD candidates will investigate the Southern Norwegian North Sea source-to-seep system through analysis of new and existing data from four different