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of older persons. The candidate will also contribute to teaching activities related to machine learning or other areas depending on the candidate’s profile. Moreover, the candidate is a team player that
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RNA-Seq, ChIP/DAP-Seq protein-DNA interaction data, bulk, and single-cell ATAC-Seq) and the application of diverse supervised machine learning approaches (e.g., feature-based, deep learning, and
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within the IN-DEEP project you will be at the forefront of developingnew hybrid machine learning (ML) accelerated solvers. A fast-expandingarea of research is the application of ML techniques to predict
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parts. First, the candidate will develop machine learning models to assist gynecologists and embryologists in their decisions and advice regarding couples with fertility problems. We will focus
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are currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning. In particular, we develop machine learning methods to derive
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microstructure, immunostaining, and neuroscience), and computer science (data analysis, machine learning, and artificial intelligence) to achieve a breakthrough in microstructure imaging with MRI. Position You
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processing of MRI and light-sheet microscopy), biological and medical sciences (tissue microstructure, immunostaining, and neuroscience), and computer science (data analysis, machine learning, and artificial
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of MRI and light-sheet microscopy), biological and medical sciences (tissue microstructure, immunostaining, and neuroscience), and computer science (data analysis, machine learning, and artificial
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machine learning aided data analysis. Correlation with other material and device characteristics. Reporting and publishing of results in leading scientific journals and conferences. Type of work: Literature
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faculty and university research policies . You act with attention to quality, integrity, creativity, and cooperation. You are fluent in Python, machine learning, and deep-learning tools (e.g., TensorFlow