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Field
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. Signal processing, AI, and sensor systems: You possess strong expertise in signal processing, particularly using statistical methods and machine learning / artificial intelligence techniques. You also have
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research on exciting projects and develop customised products and services for our clients from numerous industries and the public sector. The overarching topics at Fraunhofer ITWM are machine learning
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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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to environmental cues. Innovation drivers include the development of advanced technologies and the full integration of complex computational approaches to answer relevant biological questions. To learn more about
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missions. Prior experience with methods of statistical inference using simulations or anomaly searches with machine-learning approaches is desirable.
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Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research into this important field
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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international conferences. This can provide opportunities for networking and learning from other researchers in your field. Extracurricular Seminars and Trainings The in-house umbrella organisation INGENIUM
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imaging. Your Profile: The successful applicant must have the following: • Master’s degree in physics, biophysics, biomedical engineering, computer engineering or electrical engineering. • Excellent track