Acute Care Solutions Intern

  • Location
    Cambridge , Massachusetts
  • Category
    Engineering - Bio-Medical
  • Job type
    Contract/Temporary

About Philips Research North America: Philips Research is one of the world’s leading corporate research organizations and has a rich history of producing successful innovations in the area of health and well-being. Powered by the intellect and hard work of more than 1,600 talented individuals, we fuel value creation and growth in Philips by creating new technologies that improve people’s lives. Philips Research North America is one of Philips’ six worldwide research laboratories where innovations and discoveries help drive Philips products and opportunities. It is located in Cambridge, MA overlooking the Charles River walking distance from our collaborators at MIT. About the Department and Project: The Acute Care Solutions department researches meaningful and innovative solutions to help clinicians improve the care of acutely ill patients. Position Responsibilities: Prototyping novel algorithms and technologies for eHealth (e.g. health behavior change) applications. Assist with small-scale field studies to evaluate eHealth technologies. Assist with development of novel predictive models for eHealth technology efficacy and engagement. Communication of findings and issues to team members and external stakeholders. What we are looking for: The candidate must be a graduate level (M.S. or Ph.D. program) student in Computer Science, Biomedical Engineering, Electrical Engineering, or related field. The ideal candidate will demonstrate experience with: Experience with mHealth and/or eHealth technology Software development and algorithm design Experience with relevant programming languages (e.g., Matlab, R, Python) Quantitative data analysis and/or machine learning Skills and experience in some of the following areas would be a plus: Signal processing and algorithm development for analysis of time series data Knowledge of health psychology, cognitive psychology, or related areas Quantitative research methodology and experiment design Reinforcement learning and related areas Experience working in a multi-disciplinary team Candidate must be able to work within a team environment, should be self-motivated and have the ability to plan and execute tasks independently. Candidate should also be able to communicate effectively, both verbally and written.

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Reference number US_EN_2_107604_130676