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teaching opportunities can be arranged if desired. The ideal candidate should have experience with machine learning, particularly in deep learning or related areas. No prior knowledge of cryptography is
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation
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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January
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prototypes for projects in application areas including autonomous systems, robotics, cognitive and distributed sensing, and machine learning systems, among others. Successful candidates will be responsible
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to mlcv at tu-dresden.de or to: TU Dresden, Chair of Machine Learning
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with special emphasis on applied artificial intelligence, machine learning, natural language processing, computer vision, and experiential learning addressing business and technical challenges in
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high-quality instruction in Computer Information Technology, incorporating hands-on, applied learning experiences where appropriate. Teach courses using a variety of instructional delivery methods
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with special emphasis on applied artificial intelligence, machine learning, natural language processing, computer vision, and experiential learning addressing business and technical challenges in