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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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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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technologies Development and utilization of high throughput methods for characterizing and quantifying the physicochemical behavior of food macromolecules in complex matrices. Modelling and the use of machine
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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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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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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
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Two DTU Tenure Track Assistant Professors in Autonomous Modelling and in Robotic Synthesis of Ene...
and robotics. As the successful candidate, you will develop innovative research programs spanning atomistic and mesoscopic materials simulations, machine learning, foundation and surrogate models
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(satellites, drones, etc.) through AI and machine learning; 3) validation and feasibility of the introduced technologies through full-scale pilot scale, demonstration, documentation, and simulations