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Field
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deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement
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within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
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interpretation is subjective, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor
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responsibilities will be to: conduct high-quality research in intelligent sensing and control for complex project environments develop and implement AI-based algorithms (e.g., GNNs, deep reinforcement learning
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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research. This person will also develop skills as an independent investigator over time by working with the PI on management tasks, grant writing, and student mentorship. Who We Are Michigan Engineers
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Helmholtz-Zentrum Berlin für Materialien und Energie | Berlin, Berlin | Germany | about 20 hours ago
on the data recorded in the team, you will develop and test machine learning algorithms for perovskite tandem solar cells' energy yield and degradation Data cleaning and preparation Assisting integration
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experiments as well as experiments that are part of a related clinical trial. They will assist in mentoring PhD students to perform research. This person will also develop skills as an independent investigator
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differential problems. 2) Development of adaptive mesh generation algorithms for distributed order fractional differential equations. 3) Analysis of the stability and convergence properties of the developed
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation