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: Required: • MSc (or equivalent) in: Computer Science, Cybersecurity, Machine Learning, or related field • Strong background in: machine learning / deep learning, mathematics (probability, linear algebra
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learning and deep learning models for renewable energy forecasting, particularly wind power generation. Working with data-driven weather prediction models and high-resolution meteorological datasets
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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the development, evaluation, and practical application of machine learning methods (especially deep learning) Publication of scientific results in renowned international journals and conferences Teaching of courses
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(particularly Deep Learning), will also make it possible to leverage the collected data to enrich knowledge of ovine behavior. The candidate will join a dynamic research group within the Image/Vision team
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, computer science, data science, or similar · Strong publication record in peer-reviewed conferences and/or journals · Experience applying machine learning methods (especially deep neural network approaches
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host chromatin pathways (DFG Research Unit DEEP-DV, FOR5200). The group uses experimental infection systems, an array of high-throughput sequencing methods, and single-molecule live-cell imaging
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across the university. The RAD Collaboratory will be comprised of different research areas, each led by a faculty area lead. The vision of the RAD Collaboratory is “Deep Learning, Deep Connections
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passion for innovation. To excel in this role, you will need to possess a deep passion and vision for innovative learning. Effective collaboration and networking skills are essential as you build
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) enables computations to be performed directly on encrypted data without knowledge of the deciphering key, offering significant potential for privacy-preserving deep learning. However, conventional neural