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Field
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field Experience leveraging artificial intelligence or machine learning in the development of battery electrolytes and catalyst materials Demonstrated expertise in lithium–sulfur battery materials and
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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mouse models is required. Experience in bone and liver biology or willingness to develop an interest in inter-organ crosstalk are preferred. Required skills: Basic computer skills and proficiency with
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computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely
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, Materials Science, Engineering Mechanics, Manufacturing Engineering, Mechanical Engineering, Artificial Intelligence/Machine Learning, or a related field completed within the last 5 years Preferred
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mitochondrial quality leads to neurodegenerative diseases and impairs neuronal recovery. To approach these questions we use a combination of genetics, biochemistry, cell biology and imaging in a number of models
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
our group at the intersection of statistical methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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your scientific and professional skills by: Designing and leading analyses that apply state-of-the-art generative machine learning models (e.g., VAEs, GANs, transformer-based models) to large-scale
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imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely connected