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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory
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should meet the following qualifications and possess relevant expertise: A PhD in Physics, Applied Physics, Electrical Engineering, or a closely related field, with a strong experimental research
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, drawing on machine learning where it strengthens these methods. The research supports mission-critical scenarios and feeds into an end-to-end resilience proof of concept developed together with Swedish and
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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individuals with a wide range of backgrounds and experiences. You should demonstrate: Essential Criteria: Hold, or expect to hold shortly, a PhD in analytical chemistry, mass spectrometry, metabolomics
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scientific instrument development. Applicants should meet the following qualifications and possess relevant expertise: A PhD in Physics, Applied Physics, Electrical Engineering, or a closely related field
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. Qualifications Applicants at Postdoctoral Researcher level should hold a PhD in AI enabled learning, educational technology, information systems, computer supported learning, social entrepreneurship, innovation
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computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School