PhD Data Science Intern
PhD Data Science Internship
Vail Systems Company Profile
The human voice is capable of conveying nuances and meaning that just can't be expressed through clicks and text messages. And for that reason, voice interactions have always had a special power to shape our perceptions and experiences. At Vail, we believe in the unique power of voice interactions to create more expressive, more intimate, and more efficient interpersonal interactions. Our experts work with Fortune 500 companies to help them serve their customers more efficiently and effectively through the use of various voice technologies. From basic network services, to state-of-the-art IP telephony, to cutting edge real-time analytics, Vail technology makes millions of voice interactions better every day.
PhD Data Science Intern (Chicago Loop)
Designing emotionally sentient agents is challenging. One aspect that makes it so is natural language processing (NLP), in particular how can we construct models to process and analyze large amounts of natural language data to affect dynamic, real-time emotions in various domains. Systems that respond to social and emotional cues are considered engaging and trusting. Currently, realistic synthesis of voice communication requires a large corpus of voice recordings. Generative machine learning methods may alleviate the need to collect and label
large datasets and provide realistic voice synthesis.
Vail Systems is looking for summer interns who are enrolled in a Ph.D. program in computer science, computer engineering, or related programs to work with us on some of these challenges. The student must be conducting research in NLP and should be able to understand, codify and extract meaning from multiple communication modalities (speech, text), and build new or improve existing models in affective computing using state of the art machine learning techniques. The student will work with a team of researchers to gain insight from trying new approaches, and will work with practitioners to transition the research to a proof-of-concept.