INRIA, APSYS-AIRBUS and NOKIA Germany are working together for a European funding project "EIT Digital- Industry 4.0"dedicated to predictive maintenance et more precisely to Root Cause Analysis.

The goal of the project is to develop a set of data-science methods, algorithms and plugins to enable manufacturers identify why and where a failure or anomaly occurred and ease dysfunctional goods reparation.

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An Inria spin-off, PAM is a software publisher dedicated to predictive maintenance for industry. On the strength of several collaborative ventures with large-scale industrial firms, we wish to continue developing a solution that integrates the latest R&D technologies and meets the needs of experts in the field.

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Company: 
NIO
Date Posted: 
June 1, 2018

The next generation computing platform will be the autonomous driving electric vehicle. With data from in-car sensors, a mobile internet connected car, increased computing power, and new user interfaces that support a seamless autonomous driving experience, they will dramatically improve our lives. After home and work, the car is where we spend the most time, and we will be working on making that experience pleasurable. NIO is much more than a car company.

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GIS conducts applied research in the area of sustainable manufacturing, and also provides technical assistance in a wide variety of areas to industry. We are looking for an engineer or scientist with a strong experimental and analytical background to support existing research programs in prognostics and health management (PHM), and data analytics. Our two primary application areas are transportation systems, including DoD systems, manufacturing equipment and processes, and embedded product monitoring.

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Company: 
PARC, a Xerox Company
Date Posted: 
March 5, 2018

Senior Researcher with PhD in Electrical, Mechanical Engineering or related fields with experience in prognostics, predictive analytics, diagnostics, image/data

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Company: 
SGT Inc.
Date Posted: 
February 25, 2018

NASA is actively involved in researching and developing validated tools, technologies, and techniques for automated detection, diagnosis and prognosis that enable mitigation of adverse events during flight for current and next generation aerospace systems. Adverse events include those that arise from system, subsystem, or component faults or failures due to damage, degradation, or environmental hazards that occur during flight.

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Company: 
PARC, a Xerox Company
Date Posted: 
December 21, 2017

Exciting internship opportunity available at PARC in System Analytics, Anomaly Detection, Failure Modeling, Monitoring, Prognostics, Diagnostics, and Action Recommendations

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One postdoctoral position is available at the Singapore University of Technology and Design (SUTD) for highly qualified candidate in the field of Failure Diagnosis, Prognostics and Health Management (PHM). The selected candidate should have a relevant background in applied mathematics / Bayesian statistics / industrial engineering / data analytics and be interested in applying different mathematical tools and techniques to real-time remaining useful life estimation of a wide variety of electronic and mechanical devices and systems, in general.

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The Institute for Energy and Environment (Department of Electronic and Electrical Engineering) in collaboration with the Maritime Safety Research Centre (Department of Naval Architecture, Ocean and Marine Engineering) at the University of Strathclyde (Glasgow, Scotland, U.K.), offers a Ph.D. position in the area of prognostics and health management methods for reliability, availability, maintainability and safety (RAMS) of maritime systems.

PhD Research Description

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Company: 
Lockheed Martin
Date Posted: 
April 26, 2017
Contact: 
Roni Hendrickson - Roni.Hendrickson@lmco.com

Job Description
• Primary role is advanced member of the F-35 Prognostics & Health Management (PHM) Capability Integration Team as the Data Analytics Integrator.
• Primary responsibility is to lead the maturing of the F-35 PHM capability by understanding the aircraft systems/subsystems design, PHM design, actual PHM performance, and developing required corrective actions and automating team tools to improve efficiencies.

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