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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 2 months ago
Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills, Training/Education
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of an undergraduate degree in a related field. Training in causal inference and/or machine learning methods Effective oral and written communication skills Outstanding academic credentials and intellectual creativity
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have a background in qualitative research and community engagement. An interest in patient engagement and participatory action is a benefit, and either experience in, or interest in learning mixed
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will conduct the lab experiment for RAS system for pollution control in recycled water in aquaculture system. He/she will also use machine learning tools to predict and optimize the RAS system. Job
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bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms Downloading a copy of our Job Description Full
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, and semi-structured interviews. Using European competence models (e.g., GreenComp, LifeComp), the project will develop flexible lifelong learning models that universities can adapt to different
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collaboration between industry, government, and academia. The Australian Institute for Machine Learning (AIML) at the University of Adelaide is the largest computer vision and machine learning research group in
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AI/machine learning, and data analysis using MATLAB or Python. Provide technical assistance on related research projects, such as preparing progress presentations and reports for funding agencies
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Responsibilities: Integrate and analyze large-scale multi-omics datasets (genomics, transcriptomics, epigenomics) to derive biological insights Apply statistical and machine learning models to identify cancer risk
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, China [map ] Subject Areas: Physics / Atomic Molecular and Optical Physics , artificial gauge fields , Cold Atom Physics , Condensed Matter Theory , Machine Learning , Quantum Hydrodynamics , Quantum