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synthesis methods for the Abstraction and Reasoning Corpus (ARC) challenge . ARC is a benchmark designed to measure an AI system's ability to efficiently acquire new skills outside its training data
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to Professor Tuomo Hiippala . The project develops novel methods and resources for studying multimodality, or how humans communicate using combinations of multiple ‘modes’ of expression. These methods and
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how natural selection shapes sex-specific immune strategies. The goal is to generate quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from
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of Languages and funded by an ERC Consolidator Grant awarded to Professor Tuomo Hiippala . The project develops novel methods and resources for studying multimodality, or how humans communicate using
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(at 23:59 UTC +2). Requirements A PhD in computer science or a related topic Prior experience in logical reasoning is essential Demonstrable research experience in SAT/ASP/SMT/MaxSAT Excellent English
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the candidate's background and interests, ensuring a collaborative and engaging research experience. We seek candidates who have completed a PhD in ecological statistics or environmental economics or a related
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PhD degree in immunology or closely related field, or be close to graduation. An excellent academic record, a deep understanding of immunology, as well as practical experience of immune cell biology
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in vivo genetic mouse models, advanced live and intravital imaging, engineered microchip models, primary cell co-culture systems and novel microscopy and analysis methods. The research will provide
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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research visits at Karolinska Institute. • Excellent opportunities for career development, mentoring, and training in advanced structural and cell biological methods. • Occupational health care, access