System Analytics and Condition-Based Maintenance Intern
internships | Palo Alto, CA | Full Time
Internship opportunity available at PARC in System Analytics, Anomaly Detection, Failure Modeling, Monitoring, Prognostics, Diagnostics, and Action
PARC's System Sciences Lab (SSL) is unlike any organization in the world. Combine the best of an esteemed research organization, a top-notch university, an entrepreneurial startup, and a premiere technology company and you get SSL, a team that melds its world-class innovation chops with a focus on researching and developing real world applications that have market potential. Research in SSL focuses on artificial intelligence, modeling, design, condition management, machine learning, control, planning, optimization, and high-performance analytics for a variety of cyber-physical system applications serving industry leaders in product lifecycle management, computer-aided design, manufacturing, transportation, energy, and aerospace/defense sectors. The lab's focus extends beyond invention: the team creates new business options and opportunities.
We are working on innovative analytics projects within SSL's Analytics for Condition Evaluation of System (ACES) research area to detect anomalies, model failures in complex systems, monitor the health condition of devices and systems, enable accurate inferences about their state, and deliver recommendations for decision support. The analytics will focus on processing/fusion of multiple sources of data from devices/systems, building models, and machine learning approaches, which allow insight generation and accurate “actionable" recommendations.
The ACES research area is a growing group of researchers within SSL, with world-class strengths in diagnostics, prognostics, fleet management, data analytics, model-based design and manufacturing of complex engineering systems. The work in ACES covers a broad range of technology maturity, from abstract algorithm formulation all the way down to embedded software implementation on physical hardware platforms that span various application domains such as intelligent transportation systems, manufacturing, energy, heavy machinery, and power systems. More details on the application of ACES to condition-based maintenance are available at http://www.parc.com/cbm
- Assist in designing, developing, and delivering innovative approaches, algorithms, methods, and models as needed for state monitoring, prediction, and recommendations
- Survey and implement recent anomaly detection algorithms
- Develop and implement parameter tuning and model selection algorithm for anomaly detection
- Implement efficient data preprocessing and wrangling software for given sensor data sets
- Implement backend system such as server and database for proof-of-concept demonstration
- Document/present findings and results
- Graduate students who have completed at least 3 years of study, in Computer Science, Electrical Engineering, Mechanical Engineering, or related fields
- Background in system modeling, monitoring, prognostics, diagnostics, machine learning, signal processing, computer vision, or optimization
- Hands-on software and algorithm development
- Programming experience in Python, R, C/C++ Modelica, or Matlab
- Hands on experience in machine learning package (e.g. Scikit-learn, Keras, NIMBLE), open-source database (e.g., Influxdb, Cassandra), and/or sensors (e.g. magnetic, gyroscope, acceleration) is a plus
- Experience with prognostics and health management (PHM) concepts and tools is a plus
As one of the most prolific innovation centers in the world, PARC offers an exceptional internship experience. Considered valuable members of our community, interns are fully integrated into the daily activities of PARC's highly collaborative, multidisciplinary culture. For more information on the PARC internship program visit http://www.parc.com/internship
We offer a very competitive salary package and full benefits (medical, dental, vision, life & disability insurance, 401K. PARC also strives for the best possible work-life balance, so employees benefit from maternity, paternity and adoption leave, as well as a variety of flexible working options. PARC employees enjoy the use of onsite health & fitness center and collegial dining at our onsite cafeteria. The PARC campus is in a spacious modern building in close proximity to the resources and opportunities of Silicon Valley and benefits from nearby leading universities such as Stanford and Berkeley. It is also close to many amenities, top schools, and outdoor activities (see http://www.parc.com/about/culture.html). PARC provides a highly diverse environment and is proud to be an equal opportunity employer (see http://www.parc.com/about/careers/).
PARC, a Xerox company, is in the Business of Breakthroughs®. Practicing open innovation, we provide custom R&D services, technology, expertise, best practices, and intellectual property to Fortune 500 and Global 1000 companies, startups, and government agencies and partners. We create new business options, accelerate time to market, augment internal capabilities, and reduce risk for our clients.
Since its inception, PARC has pioneered many technology platforms – from the Ethernet and laser printing to the GUI and ubiquitous computing – and has enabled the creation of many industries. Incorporated as an independent, wholly owned subsidiary of Xerox in 2002, PARC today continues the research that enables breakthroughs for our clients' businesses.
PARC is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. People with disabilities who need a reasonable accommodation to apply or compete for employment with PARC should contact PARC Human Resources.