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Field
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reinforcement learning. The project will investigate a simulation platform that reproduces the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data
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for Transportation (VITA ) is looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot
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. The applicant should be holder of a PhD in artificial intelligence/machine learning and possess excellent background in agentic systems, large language models and/or continual learning besides analytical
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optimization of large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long
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with national and international teams of mathematicians and computer scientists PhD applicants must possess a Master's degree in mathematics, theoretical physics, computer science, or a related field
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: Conceptualisation and synthesis of data integration and visualisation workflows. Data management and the development of knowledge graphs Development and application of AI and machine learning methods and pipelines
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field of research Participate in education, and PhD and master student supervision Profile Strong background in computational biology, bioinformatics, machine learning, or a related quantitative field
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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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research in the natural, mathematical and computer sciences with a focus on the processing, structuring, and analyzing of large amounts of complex data and the development of computational methods and