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human health. Within this mission, the Iorio Group works at the intersection of computational biology, functional genomics, and precision oncology, integrating machine learning, large-scale CRISPR
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highly multidisciplinary environment, collaborating closely with radiologists, software engineers, and data scientists to advance clinically relevant AI methods. Your research is expected to lead to high
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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. Weinstein (DTU Chemistry) to develop novel molecular ML methods and use them to control, steer, and learn from large scale wetlab experiments. You will deploy these methods in collaboration with Prof. Katrine
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, epidemiology, biostatistics, statistics, computer science, machine learning, or another related statistical or computational discipline Creative, thoughtful, and a communicative collaborator Familiarity with
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transcriptomics, spatial gene expression, and biomechanical measurements. Implement physics-informed machine learning models to predict mechanical properties from cell morphology. Collaborate closely with
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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and uncertainty mapping at satellite, airborne and drone levels. You will explore advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and
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a postdoctoral scholar in computational biology. The PI, Dr. Lixing Yang is an Associate Professor at the Ben May Department for Cancer Research and the Department of Human Genetics. To learn more
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interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and translational impact. Close collaboration with Helical-AI will ensure that developed models