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Field
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will have the opportunity to develop innovative algorithms and models that integrate multiple data modalities, collaborate with industry partners, and contribute to high-impact publications. Job
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will have the opportunity to investigate innovative solutions using machine learning algorithms and predictive modelling techniques in the context of a collaborative project with Goodyear Luxembourg (one
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the mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world
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algorithms. The candidate should have the following skills: • Strong technical, analytical, and quantitative abilities; • Strong interpersonal, organizational, and communication skills, and the willingness
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microscopy. The ideal candidate will be highly creative, hardworking, and a team player. They will also have the ability to manage multiple projects and students in pursuit of team goals. Representative
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. This work involves developing novel techniques, algorithms, and software packages that enable more robust and scalable approaches to cybersecurity using AI-based techniques. In addition to technical
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, molecular properties, and pathological images. Strong knowledge and experience in data science algorithms, methods, and analysis techniques. Experience in programming using Python and R languages and working
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approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps of mammalian brains using mice as an animal model. Three
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within tissues using our in-house developed spatial transcriptomics-based technology (Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class
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: Employing satellite observation and machine leaning to improve model representation of ice sheet surface melt”. The candidate will work under the direction of Prof Nowicki and in collaboration with multiple