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the objects. Many real world networks have a multidimensional nature such as networks that contain multiple connections. For instance, transport networks in a country when considering different means
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anomalies in evolving graphs. In this research proposal, our aim is to explore the parallels of deep learning and anomaly detection in dynamic graphs. In particular we are interested to redesign deep neural
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or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high-throughput modeling over
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signals are required for C. difficile germination. In nutrient-rich media, genetically identical spores germinate at different times, but in presence of bile salts—exclusively found in the gut—spores
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of the bioinformatics laboratory at the college of computing (bioinformatics.um6p.ma). The project aims to build a simulator for the response of mutations at different levels. The candidate will be
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or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high-throughput modeling over
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in nematodes. Much of our work now focuses on the evolution of egg-laying behaviour and the transitions to viviparity. In parallel, we are also interested in characterizing the natural history, ecology
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viral vectors, electroporation with different repair templates, and evaluating strategies to enhance homology-directed repair. Through this work, you will gain advanced training in molecular and cell
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Description This postdoctoral position is part of the research portfolio within Mechanical Engineering and Product Development, where multiple externally funded research projects are conducted in parallel in
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because these companies need to allow their users to write simple, high-level code and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep