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-correction. This PhD falls into the fields of error-correction and Deep Learning. Due to the inherent unreliability of the DNA storage support, the goal will be to develop advanced deep learning models
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datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning). The goals are to develop new computational methods that allow the scientific inference
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. The project is led by Heiko Schütt and will employ one PostDoc and one PhD student. About the role... You will develop new Bayesian methods to compare deep neural network and other artificial representations
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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
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strong interest in computer science (software development, machine learning techniques, etc.) is desirable. · Applicants must have a maximum of 3 years of research experience after the PhD. · Language
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on advanced AI methodologies. Incorporating physics-guided deep learning models that explicitly integrate the underlying MRI signal formation process to enhance reconstruction reliability and interpretability
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an industrial track (2 years at IMT Atlantique + 12 months at Sony STC, Germany). 1.1. Domain and scientific/technical context Generative and creative systems based on Deep Learning have recently emerged under
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DeepTypo project, which aims to use deep learning models to study the typography of languages. The work will focus on creating a bidirectional system that (i) from an audio recording will predict
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computer science (notably from the artificial intelligence and deep-learning field), requiring the collaboration of experts with different expertise. The ambition of the project resides in popularizing AI
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duration to learn about the use of the XEMIS1 setup (start ~sep-2025) Start in parallel to point 1., a joint PhD thesis (SEED funding from Sept 2025) : the student will start at SUBATECH to work on the