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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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knowledge of Microsoft Word, Excel and PowerPoint. You have experience in, or are willing to learn MATLAB. You hold the FELASA B certificate, or international equivalent or you are willing to follow a
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neuromorphic ultra-low-power active sensor readout and processing at the edge. The chip design will enable online learning capabilities, aiming at modulating the spatio-temporal filtering properties with
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international workshops and conferences, presenting and discussing your research globally. Teach and contribute: Provide support for teaching activities and teaching innovation. Build up and apply skills: Build
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, our people are at the heart of every breakthrough. Position You will work actively on the preparation and defence of a PhD thesis in the field of robust reinforcement learning (RL). This project
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eager to learn, resilient and motivated to successfully complete a multi-year research project. You demonstrate ownership of your learning and can handle feedback. You are able to prioritize and work
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diverse, international, and multidisciplinary research team • Opportunities to collaborate with scholars of different levels and with communities in six countries • We also offer a supportive learning
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-driven approaches to health, society, and policy. BISI combines expertise in epidemiology, biostatistics, health economics, and machine learning to tackle complex societal challenges. BISI is actively