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programming languages such as Python and C++, as well as experience with machine learning frameworks like TensorFlow or PyTorch Familiarity with image processing libraries and a solid grasp of deep learning
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challenges. It fosters collaborations and develops advanced computational tools through a hub for multi-omics and systems biology. Project description The PhD project aims to explore how multiple layers
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for aircraft systems. You will integrate heterogeneous data streams (from multiple sources, such as sensory, physics model, etc.), flag impending failures, pinpoint and trace back the origin of system faults
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at the intersection of neuroscience and AI, with opportunities for innovation and collaboration across multiple disciplines. Candidates are expected to have experience in cutting edge AI technologies and their
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transportation operations and network modelling, accessibility analysis, data analysis (statistics and/or machine learning methods), and spatial mapping. Because the work will involve multiple years of daily
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
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description The PhD project aims to explore how multiple layers of gene expression regulation—including DNA packaging, transcription initiation, and translation—interact to control gene activity. Using
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, clustering), statistical modelling, and other computational techniques Process large scale text data sets in multiple languages Create documentation for data and processes guided by principles
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Open Source Geospatial software and libraries, e.g. GDAL or other open source geospatial packages under OSGeo; Experience in programming with multiple languages (e.g. Java, C/C++, Python) for geospatial