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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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technology and innovation. Home to the Wall of Wind and Institute of Environment, FIU stands at the forefront of discovery and innovation. With a focus on student success, economic mobility and community
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work environment. We are seeking a part-time Research Assistant in computer vision and machine learning for human behavior analysis and modeling. The successful candidate will investigate new algorithms
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
emphasis on undergraduate education. The Department has tenured and tenure-track faculty, academic fixed-term faculty and research faculty. The current student body comprises approximately 120 graduate
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Committee, and other GME and health system committees. Qualified candidates must be a role model for professionalism. He/She will deliver the program's curriculum and care for its patients in a fashion
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programme. Details of research projects currently being undertaken can be seen at: https://www.crick.ac.uk/vivian-li/ . Key Responsibilities In this project, some of the specific aims include but are not
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, artificial intelligence (AI), and socio-economic analysis. This program will equip them with advanced expertise in reservoir modelling, machine learning, advanced oxidation processes (AOP), and microbial
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nanoparticles - Experience with in vivo validation of nanomaterials in animal models of vaccines - Experience with tumor models, in vivo and in vitro - Experience with multiparametric flow cytometry and sorting
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partners and key stakeholders, including the medical group, hospital, and SOM/HSC; develop rational, data-based and realistic financial models of proposed initiatives to ensure sustainable investment
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(PDEs) and modelling with PDEs. The applicant’s research focus must be a specialisation in numerical analysis for PDEs. The specialisation should both strengthen the division’s current research in