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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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deep learning approaches, with a particular interest in developing methods capable of handling scarce or corrupted data, designing methods for specific imaging modalities, or understanding and
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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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deep learning, and its extensions for these additional targets. In particular, we have a large collection of newspaper articles dealing with migration-related topics, and we are investigating how text
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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modeling of post-tuberculosis lung disease risk using approaches including generalized linear models and deep learning. Performs other job-related duties as assigned. Minimum Qualifications MD or Ph.D. in
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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for Transportation (VITA ) is looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot