GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA
Revision and Expansion of Company’s Predictive Maintenance System Architecture
Thesis in external company
External reference persons Davide Mazzucchi (email@example.com)
Description Zirak is an Italian SME company, established in 2000, with an Italian headquarter and a Portuguese R&D lab, providing high quality Information Technology services and extensive support on automotive and IoT related topics, from architectural analysis and requirements management to implementation and validation of customized state-of-the-art solutions.
We have contributed to several M2M projects (e.g. dynamometric key interfacing system, sanitization system for hospital unit spaces and electric bike-sharing platforms), and we have designed our own IoT plaform for Predictive Logistics in collaboration with major European R&D centers.
In the context of an on going project of the Zirak IoT Department, we propose a thesis in the IoT sector, which sees as its objectives:
1. The in-depth review of the current Machine Learning Algorithms and Predictive Maintenance System Architecture used by Zirak.
2. The proposal of an additional Prediction of Remaining Useful Lifetime (RUL) or Runs to Faliure (RTF) Stage, based on the current output of the System and/or additional External Data Bases, keeping as a cornerstone the flexibility of the solution to handle different type of data so as to satisfy different and futuristic Predictive Maintenance use cases.
3. Perform the Implementation the proposed solution into Zirak’s backend and provide a Demo by using company private data.
4. Display the outcome of the proposed stage on a Model Dashboard.
See also www.zirak.it
Required skills The candidate is required to have the following skills and competences:
• Good knowledge of Python.
• Good knowledge of Artificial Intelligence methods and Machine Learning techniques.
• Good attitude to teamwork.
• Good level of spoken and written English.
• Basic knowledge of Jupyter framework.
• Basic knowledge of the following libraries: Scikit-learn, TensorFlow, Keras or similar.
• Basic knowledge of Matlab and Spark framework.
Notes availability to travel to Mondově part-time (60% time).
Deadline 09/03/2021 PROPONI LA TUA CANDIDATURA