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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning
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data processing, image/signal analysis, and machine learning. ✅ Familiarity with instrument control, calibration, and automation workflows. ✅ Excellent written and oral communication skills in English
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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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Machine Learning algorithms Experience with one or more deep learning libraries such as PyTorch, TensorFlow, or Keras Hands-on experience with neuroimaging processing pipelines Excellent writing and
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to efficiently collect/organize/analyze data and initiate new/modified procedures/techniques based on the latest developments are expected. Certificates/Credentials/Licenses n/a Computer Skills General office
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Qualifications • Highly recommended by scientists in the candidate’s field of study • Expertise in one of the following areas: Environmental or Performance Physiology, Machine Learning, Motion Analysis, Multiscale
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multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the interface of computational
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and quantitative gene expression analysis Excellent organizational, communication, and teamwork skills Certificates/Credentials/Licenses Computer Skills General office suite and willingness to learn lab
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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the