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The Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark, Odense, invites applications for a PhD position in algorithms. The position has a duration of 3
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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datasets with varied structures, requiring different analysis approaches. We are looking for a person who is motivated to develop efficient analysis solutions for the P61A data in Python. You can build on
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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. The architecture of the platform will follow the existing design, based on the following technologies: o Backend: Node.js microservices exposing REST APIs o Frontend: different frontends (HMIs) including
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Assistant Professor (tenure-track) and Associate Professor (tenured) Positions in Computer Scienc...
of Mathematics and Computer Science (IMADA), and other research sections at the department are Algorithms Computational Science Data Science and Statistics Geometry, Topology and Algebra Learning Experience Design
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new challenges in personalized oncology through the MASTER program. This prospective registry trial employs very comprehensive molecular profiling and integrated data analysis (WGS/WES, RNA sequencing
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, analysis and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology
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environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental sustainability. You will focus on processing