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transform visual information, and how neuromodulatory state changes, including exposure to drugs of abuse, alter visual processing and visual behavior. We combine large-scale electrophysiology with controlled
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education (co-op) program in the country with students earning $94 million annually through paid experiences, an academic health system, and as a member of the Big 12 Conference. The university contributes
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metric benchmarks for OPTIR measurements that can be used for evaluating data and method transferability to widely used IR-based and Raman techniques - Contribute to NIST efforts for evaluating and
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) equipped with a cryogenic stage for surface analysis - Develop computer code (e.g. Python) for the development and analysis of optical cavities Qualifications § PhD degree in Materials, Electrical
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, machine learning and AI, statistical computing, big data and AI applications and prediction in biology, medicine and infectious diseases. Potential research projects include (but are not limited
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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team of chemists, physicists, mathematicians, data scientists and machine learning experts characterizing PFAS molecules used in the semiconductor industry with the goal of discovering new molecules
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nanofabrication techniques and processes. § Data analysis with MATLAB, Mathematica, Python or similar software. § Strong oral and written communication skills. Application Instructions Please upload
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scalable bioinformatics pipelines on cloud-based infrastructure. The Research Fellow will be responsible for the code base supporting the large-scale genomic processing and analysis pipelines at the SMaHT
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | 11 days ago
therapeutics and generic drugs. These efforts cover more than just medicines. Research Project: This project aims to use large language models (LLM) to extract and analyze discussions on social media platforms