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specializes in integrating machine learning techniques with circuit QED theory to identify optimal regimes for hardware control and development. With their exceptional skills in device modeling, the team
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. The current project deals with the use of machine learning / artificial intelligence and big data science in the field of synchrotron research. Development and application of machine learning / artificial
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at the forefront of international AI research. We are looking for an enthusiastic researcher who is quick to grasp new concepts and ideas and can solve complex deep learning problems with high-quality
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the potential to apply these methods to different domains. Specifically, you will: Develop, implement, and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL) for scientific and large
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genomic data using deep learning methods in order to identify degrading enzymes from different data resources Using Hidden Markov Models and similar tools as well as machine learning for the identification
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dynamics of energy materials on the atomic and mesoscopic scale using neutron methods, complementary X-ray experiments and support of further techniques including computer simulations Synthesis and