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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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architectures for deep learning. Deploy your models onboard robotic systems. Publish your findings at top-tier venues. Disseminate your research findings at national and international workshops and conferences
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, mathematical criteria stability and robustness of neural networks, applications of topology and geometry to deep learning, the topology and geometry of data, or the dynamics of learning. The successful candidate
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, mathematical criteria stability and robustness of neural networks, applications of topology and geometry to deep learning, the topology and geometry of data, or the dynamics of learning. The successful candidate
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international specialists. Within Cumulus, you will lead the development of “downscaling” methods for sub-seasonal (2-4 week) forecasts. Our priority will be to implement deep-learning based methods, to turn
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Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill
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skills and knowledge of computer programming such as Unix, Python and R Previous experience with multi-omics and their integration Deep technical understanding of bioinformatics analysis approaches and
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/rehabilitation applications. It is expected that the application has the knowledge of: 1) deep learning, large AI models, large language models; 2) the rendering techniques for generating human body animations
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hardware design (Verilog/VHDL), FPGA-based acceleration, etc. Experience with deep learning frameworks like PyTorch, Keras, or TensorFlow, and tools such as Jupyter Notebook, is expected. A strong foundation
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-life environments. The Role: As Research Fellow on the COG-MHEAR project, you will have the opportunity to use your strong background in deep neural networks and multimodal hearing-aid signal processing