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research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 2 hours ago
-throughput screening; - Cell painting assays and high-content image-based analysis (e.g., CellProfiler, Harmony); - Machine learning models for antimicrobial activity prediction (e.g., Weka); - Strong
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research grants in the above areas Job Requirements: A PhD degree in Computer Science, Data Science, Engineering, or a related field. Research experience in Computer Vision, Image Processing, Multimedia
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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UiO/Anders Lien 1st March 2026 Languages English English English PhD Research Fellow in reinforcement learning for generative systems in physics at the INTED Center Apply for this job See
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, including protein structural analysis Job Requirements: PhD in Biological Sciences, Bioinformatics, Computational Biology, or related fields Prior research experience in machine learning or systems biology is
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digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different conditions. To propose a methodology/framework in a
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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investigate the possibility of using microbial biosignatures to identify sites with past and current hydrocarbon leakage by developing new machine learning models in collaboration with the research group