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learning, deep learning and relevant software framework (R and Python) is highly desired. Very good oral and written communication skills in English are required. Emphasis will also be given on personal
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team, you will apply cutting-edge machine learning and deep learning techniques to dramatically reduce testing cycles. You will lead life cycle analyses, implement advanced health monitoring strategies
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Applications are invited for a position in the rapidly expanding data analytics run by Prof Adam Dubis. The main focus of the team is to develop deep learning tools for prediction of disease progression
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properties through tuning of concentrations and types of viscosity modifiers and superplasticizers, deep learning modeling of parameters of cement composites. The project is realized at the Bydgoszcz
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investigate analysis techniques to automatically extract 3D models of the eye and lens from MRI scans and optical coherence tomography (OCT) using deep learning techniques. To improve visualisation of critical
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leveraging advanced computer vision and deep learning-based pose estimation from football match footage to analyze pre-injury biomechanical patterns and joint load dynamics. The research aims to create
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on leveraging deep learning and advanced image processing techniques to improve the current tools for biomonitoring of aquatic ecosystems. This position involves the development and application of machine
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that can be used for training machine learning and deep learning models. You will work in tight collaboration with other researchers in Nijmegen, Delft and at the Hubrecht Institute (van Oudenaarden group
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operation by nonspecialized personnel, and to allow automated interpretation. As a PhD student you will develop novel deep learning methods for identification of fetal growth abnormality from ultrasound
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methods based on state-space models [3] have demonstrated strong capabilities in modeling very long sequences. In this context, these methods provide the perfect alternative to standard deep learning