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Field
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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exposome and dynamic exposome modeling, learning in timeseries and spatial data, and hybrid deep learning-causal modeling. The successful applicant should have significant research experience in at least two
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 5 days ago
. [8] Michaela Blott, Thomas B Preußer, Nicholas J Fraser, Giulio Gambardella, Kenneth O’brien, Yaman Umuroglu, Miriam Leeser, and Kees Vissers. FINN-R: An end-to-end deep-learning framework for fast
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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. Knowledge on multiphase (gas-particle two phase system), thermal energy storage, and/or renewable hydrogen technologies. Familiar with application of machine learning and deep learning algorithms to fluid and
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may also be appropriate for research projects requiring long-term strategies of building trust to gain access to the object of research. Fieldwork may consist of deep immersion in one place or research
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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impact, leveraging one of the highest-quality financial datasets in the industry. What You’ll Do Conduct research and develop ML models to enhance trading strategies, with a focus on deep learning and
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… Requirements Specific Requirements A talented, motivated, enthusiastic, curiosity-driven researcher. Deep analytical skills, initiative, creativity, and flexibility are highly desired. A Master's degree in
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies