Bosch Research Pittsburgh would like to invite an enthusiastic and knowledgeable research intern for investigations at the intersection of Knowledge Representation and Reasoning, Machine Learning, and Natural Language Processing. We wish to develop algorithms for making-sense of heterogeneous information, spanning from textual resources (e.g., FAQ, emails, manuals, crowdsourced annotations) to sensor-based data (e.g., streamed by smart environments, wearables, autonomous vehicles). We wish to integrate such algorithms to an open source chatbot interface, enabling decision support in the Information Space through basic Q/A interactions.
We expect the intern to perform implementation and evaluation of various methods, inspired by his/her own insights, team discussion, and contemporary academic literature. Viable methods may comprise: machine learning, including traditional approaches and more recent deep neural methods, semantic web technologies, crowd-based intelligence. Regardless of the method(s), the intern must understand the relevant challenges of developing a hybrid architecture that leverages symbolic and sub-symbolic approaches to Artificial Intelligence (AI).
Together with our faculty collaborators in the Language Technologies Institute (LTI), in the School of Computer Science at Carnegie Mellon University (CMU), we have made several key developments that we expect the prospective intern to leverage and extend. The final, key component of the internship is scientific contribution; the prospective intern is expected to work with teammates to publish a high-quality research paper.
By choice, we are committed to a diverse workforce - EOE/Protected Veteran/Disabled.
BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics) Initiatives