Artificial Intelligence Intern
Location: Cambridge, Massachusetts
Posted: November 25, 2016
Reference ID: 1661997799
As a diversified health and well-being company, Philips is focused on improving people's lives through meaningful innovations across three interconnected sectors: Healthcare, Lighting and Consumer Lifestyle. Philips opened its first U.S. office in 1933, and today, the United States is the company's largest single market in the world, with more than 22,000 employees and operations at 50 major facilities in 22 states. At Philips, we believe that in order to make something for a person, you first have to be one. That's why, we empower our employees to build personally meaningful careers by encouraging them to take the experiences and interests that motivate them in their daily lives and apply them professionally. The Artificial Intelligence Laboratory at Philips Research North America is creating and optimizing solutions for clinical reasoning across the healthcare continuum to support knowledge discovery, decision-making and attainment of optimal patient outcomes. The candidate will collaborate on novel solutions focused on implementing Natural Language Processing (NLP) and Machine Learning techniques to address tasks related to (but not limited) information extraction, text summarization, natural language generation, automated question answering, and reasoning using unstructured clinical documents, biomedical literature and social media data. Position Requirements (Degree/Experience/Skills): The candidate must be a PhD student (2nd year or above) in Computer Science with expertise in computational linguistics, natural language processing, and machine learning (especially, deep learning). Proficiency in NLP-related software tools (e.g. NLTK, UIMA), deep learning frameworks (e.g. Theano, Caffe, Torch, TensorFlow or CNTK) and software development experience with Python, Java or Lua etc. are highly desirable. Experience in dealing with clinical text data and familiarity with standardized medical terminologies (e.g. SNOMED-CT, ICD-10 etc.) are preferable. The candidate must be able to work within a team-oriented environment and should be self-motivated with the ability to plan, execute and evaluate. The candidate should also be able to communicate effectively in English (both verbal and written) and be willing to contribute to scientific publications.