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You will be working in a larger research and development project in parallel, distributed and heterogeneous computing. The project work can, under certain circumstances, be combined with an internal
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of scientific data, e.g. from image acquisition modalities or scientific simulations. Efficient algorithms are at the core of most of these data analysis and visualization applications. The focus of this Ph.D
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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postdocs (2 years each) research/consumables funding Work assignments As an Assistant Professor, you are expected to conduct and lead pioneering research that combines fundamental and applied aspects of soft
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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and development of algorithms, methods, and theories aimed at better understanding the properties and underlying mechanisms within statistical and deep learning-based systems also in the presence
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application! Your work assignments Our research projects focus on distributed sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine learning
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distributed across social space, whether residential, organizational, or relational. Segregation is a recurring topic in analytical sociology, offering a key example of how interacting individuals may
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-taking mechanisms, and/or experience of running distributed organizations. Ability to build communities and engender a common sense of purpose within them. Awareness of data science and AI challenges
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, postdocs, researchers, and industry. Your qualifications You have graduated at Master’s level or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in