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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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of immune cell function. These projects are focused on making safer and more effective cell therapies (e.g., CAR-T) and gene therapies for cancer and beyond. We are an interdisciplinary lab spanning
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The Centre for Machine Learning within the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark invites
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. graduates and doctoral candidates nearing graduation who have research interests in applied statistics, machine learning, or computational biology to apply for our postdoctoral fellows program. This program
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to advancing machine learning in biomedicine. The Program focuses on developing and applying cutting-edge AI approaches to address key challenges in molecular biology, clinical research, and translational
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uses long timescale molecular dynamics (MD) simulations, integrated with experimental observables (especially cryo-electron microscopy data), and machine learning tools to better capture the dynamics
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tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other
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mathematics, biophysics, AI/machine learning, computational biology, computer science/engineering, statistical inference, or related fields are particularly encouraged to apply. POSITION DESCRIPTION Flatiron
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented