Job Description

Bosch Research Pittsburgh would like to invite an enthusiastic research intern for investigations at the intersection of knowledge representation and machine learning. The intern will help to develop novel algorithms and methods relevant for autonomous driving. More specifically, they will develop Knowledge Graph Embeddings (KGE) from multimodal data of driving scenes and use these embeddings for various scene understanding tasks, such as object prediction, scene similarity, scene classification, etc.

We expect the intern to perform implementation and evaluation of various methods, inspired by their own insights, team discussion, and contemporary academic literature. Viable methods may comprise: semantic web technologies, with a focus on knowledge graphs and knowledge graph embeddings; machine learning, including traditional approaches and more recent deep neural methods. Regardless of the method(s), the intern must understand the relevant challenges of developing a neuro-symbolic AI architecture.

At Bosch Research 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 in a major conference (AAAI, ECAI, ISWC, ESWC, IJCAI, etc.)

TASKS

• Perform extensive state of the art review
• Generate a research plan, detailing intended approaches and evaluation methods
• Implement, apply and evaluate neuro-symbolic algorithms for relevant downstream tasks
• Present related work and research progress to colleagues, on a weekly basis
• Formalize findings as contribution to patent filing (if applicable)
• Summarize findings as a research paper (required)

Qualifications

• Strong background in AI, including symbolic and sub-symbolic approaches
• Experience with Semantic Web technologies (e.g., Stardog, RDFLib) and standards (e.g., RDF, OWL, SPARQL)
• Experience with Knowledge Graph Embedding algorithms (e.g., TransE, HolE, ConvKB) and toolkits (e.g. Ampligraph)
• Experience with data analytics toolkits, such as scikit-learn, MATLAB, or R
• Experience in deep learning model development, using PyTorch

Additional Information

OTHER REQUIREMENTS

• Degree level: pursuing doctoral degree, or current post-doctoral researcher
• Major: Computer Science, Electrical/Computer Engineering, Statistics, or related

LOGISTICS

• Internship location: Pittsburgh, Pennsylvania, United States
• Start date: Typically, sometime between April and June
• Duration: Typically, 14 weeks (extension possible; subsequent research collaboration encouraged)

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

• FIRST Robotics (For Inspiration and Recognition of Science and Technology)
• AWIM (A World in Motion)