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models, and animal models of cardiomyopathy, heart failure and vascular disease. During past years, the lab has published in Nature Cell Biology, Developmental Cell, Development, Genome Biology, Nature
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disciplines, including human-robot interaction, robot learning, soft robotics, computer vision, and agricultural robotics. About the PhD project: We are looking for a highly motivated and talented PhD research
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evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge
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fellow to join the 3D Stem Cell Biology Research Lab (https://www.hashinolab.com ) and study normal and pathological development of the human inner ear using stem cell-derived organoids as a model system
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working with NLP in general and LLMs in particular. They will also help to further develop machine learning models to predict clinical outcomes. Familiarity with current methods in this area is essential
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devices, and edge computing platforms. Deploy machine learning models to enhance process control and system responsiveness. Collaborate with industry partners to identify research challenges, co-develop
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resistance, utilizing human and murine cells, orthotopic models, and Genetically Engineered Mouse Models (GEMMs). We are seeking a motivated and hard-working scientist with strong project leadership and
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machine learning, coupled fire behavior/fire atmosphere modeling, air quality modeling, and system evaluation. Depending on their skills and interests, they can participate in various aspects of the project
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different