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Field
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to test system performance Knowledge, Skills, and Abilities Computer skills sufficient to work with locally-written MatLab software and commercial software for data acquisition and interpretation
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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networks, RNNs, LLMs) or in the deployment of such algorithms. Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators
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science, machine learning or a related field. Strong experience working with clinical, biomedical, genetic, electronic health record, or health administrative data. Experience with large language models
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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the application of statistics and machine learning in social science. The position requires no teaching, though teaching opportunities may be provided if requested. When teaching, successful candidates will carry
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of machine learning techniques, programming and coding for data analysis, biomarker discovery, and collaboration with clinicians and students. Additionally, the candidate will be responsible for grant writing
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position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Job description:Postdoctoral Researcher - Machine Learning for Plant Regulatory Genomics