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processes, mass transports, circular material flows and charging constraints. Methodologically, the work may combine geospatial data management, database technology, data science, machine learning, simulation
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. The following experience will strengthen your application: industrial product development or manufacturing research modelling and simulation, digital twins or digital threads AI, machine learning
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, Cybersecurity, AI, Machine Learning (ML), Data Science, or another closely related subject, no more than three years before the application deadline; has documented knowledge of AI and ML; has demonstrated
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. Python, Julia, C++, R); experience with sequencing (NGS) data, geometry or graph algorithms, statistical inference or machine learning; error-correcting codes or information theory; molecular modelling
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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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, 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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, multi-omics analyses and systematic bioinformatics techniques is a strong advantage. Excellent programming skills (Python/R) and a solid training in AI or machine learning are highly preferred. A strong
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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a doctoral student with a strong background in machine learning, mathematics, and modeling, and an interest in biological systems. The successful candidate will join a project to understand and model
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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