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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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of business models in the context of AI, platforms and servitization Entrepreneurship in emerging technology domains – opportunities and challenges for startups in AI and deep tech Strategic entrepreneurship
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characterization of defects in AlN and high-Al content AlGaN. Deep knowledge of semiconductor physics. Keen interest and/or experience in application of AI approaches. Knowledge of ultrawide bandgap semiconductors
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empirical expertise in the complex social dimensions of climate extremes, focusing on coastal and inland regions experiencing ecological and land loss. The successful candidate will demonstrate deep knowledge
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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The postdoc fellow will conduct research in the intersection of AI/Machine Learning and Software Technology. The advertised position will be placed in the DISTA research group (https://lnu.se/en/dista
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of mathematical areas. The position will be placed at the Department of Computer Vision and Machine Learning (CVML) at the Mathematics Centre (https://maths.lu.se/). Mathematics Centre is a department
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration