Knowledge Discovery Engine: Based on Computational Linguistics, Big Data Science and Graphical Network Mathematics
Leonardo Innovations has developed a knowledge discovery engine based at the intersection of computational linguistics, big data science, and graphical network mathematics. This engine enables the self-assembly of semantic knowledge networks to identify semantically relevant documents across massive document sets. Using this approach, we interconnect disparate data sets to discern otherwise hidden interconnections in a wide range of applications including research and development strategy, legal diligence, and competitive intelligence. Our customers include Fortune 100 companies in the life sciences, chemical, semiconductor, electronics, and automotive industries. For our Phase II project, we are focused on expanding both the data scope and network analytics features of our discovery engine. This work requires a fusion of computational linguistics and network mathematics. less Leonardo Innovations has developed a knowledge discovery engine based at the intersection of computational linguistics, big data science, and graphical network mathematics. This engine enables the self-assembly of semantic knowledge networks to identify semantically relevant documents across massive document sets. Using this approach, we interconnect disparate data sets to discern otherwise hidden interconnections in a wide range of applications including research and development strategy, legal diligence, and competitive... more
Our company has developed a knowledge discovery engine leveraging our proprietary large scale knowledge network. Driven by the convergence of computational linguistics, big data science and graphical network mathematics, we are offering a postdoctoral research position focusing on the mathematical and computational analyses of our large-scale knowledge network. This position will include aspects of artificial intelligence tools, such as machine learning, to identify structural motifs within the knowledge network. We welcome applications from mathematicians seeking to expand the applicability of their knowledge into computational linguistics as well as computational linguists seeking to develop rigorous mathematical frameworks to treat network structure and function.
We seek applicants with knowledge of and experience in network mathematics, artificial intelligence (machine learning), computational linguistics (natural language processing), and large scale statistical analyses. The successful applicant should be proficient in linear algebra and have software development experience in any combination of the Erlnd and/or Haskell programming languages as well are knowledge, experience, or interest in cloud computing, map-reduce frameworks, and distributed system architectures. Interest or experience in developing neural net-based learning frameworks is highly desired as well.
