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the ability to perform complex data analyses. Has experience with implementing computer-based experiments as well as field experiments. Has professional proficiency in English, both written and spoken
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/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning approaches, and development
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype
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written and spoken Willingness to engage in interdisciplinary collaboration and fieldwork Advantageous: Knowledge of bat ecology and species identification Experience with machine learning or automated
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at the top venues of machine learning research. Responsibilities and qualifications You should have prior experience with machine learning from both a theoretical and practical perspective. Experience in one
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creating a unified data framework for microbial carbon dioxide conversion and establishing a predictive AI modeling. Your profile The candidate is required to have a strong background in AI/machine learning
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creating a unified data framework for microbial carbon dioxide conversion and establishing a predictive AI modeling. Your profile The candidate is required to have a strong background in AI/machine learning
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing