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Assistant Professor in statistics for the development of privacy-enhancing techniques in health care
following areas: Synthetic data generation Machine learning Large health register data GDPR compliance rules Valued personal competencies include being independent and creative, having an outgoing personality
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent frameworks). Hands-on experience with data representation and embeddings, ideally applied
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Are you an experienced researcher in microbial genomics and bioinformatics with a strong record of university teaching, and expertise in whole-genome sequencing (WGS) analysis, machine learning and
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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methodological development and application of bioinformatics, biostatistics, machine learning, and data management within clinical research. CLINDA is interdisciplinary and employs biostatisticians
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focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be designed and deployed efficiently
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intelligent control and aerial robotics for navigation in uncertain environment. You will be mainly responsible: for implementation of machine-learning algorithms for unmanned aerial vehicles; validation