Academic Jobs

Aarhus University Au, Logo

PhD fellowship position in Embedded Artificial Intelligence – Aarhus, Denmark, 2022

Aarhus University (AU), Denmark

Applications are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 1 April 2022 or later.

Support us by Sharing!

ADVERTISEMENT
Scroll Down for Content

General Info

Position: PhD fellowship
No. of Positions: 1
Research Field:
Joining Date: Apr 01, 2022
Contract Period: -
Salary: According to Standard Norms

Workplace:
Department of Electrical and Computer Engineering
Graduate School of Technical Sciences
Aarhus University (AU)
Aarhus N, Denmark

ADVERTISEMENT
Scroll Down for Content

Qualification Details

MSc in Computer science/Engineering and a background in one or more of the following fields: model-based engineering, embedded systems, machine learning, artificial intelligence.

Responsibilities/Job Description

The mission for the PhD student is to:- Investigating, collecting and analyzing data from actual ships and test lab facility to identify how to leverage the data use for systems operation, ML training and prediction.

- Design of ML models for operation conditions & monitoring from existing data through modular mock-ups.

- Design of ML models for components performance & deterioration, from existing data, to predict damage for selected proof of concept cases.

- Testing of the maintenance predictor in actual and simulated environments to assess the accuracy of ML models and calculations. Feasibility analysis will be performed on both cloud and edge.

- Exploring state of the art tool-chains and applicability for the proposed methodology and the HW/SW infrastructure (Triton controllers) of MAN ES.

- Implementation, deployment and testing of the proposed predictive maintenance system.

How to Apply?

Online Application through "Apply Now" Button from this page


Reference Number: -
(If any, use it in the necessary place)

ADVERTISEMENT
Scroll Down for Content

Documents Required

For information about application requirements and mandatory attachments, please see our application guide.

About the Project

Artifical Intelligence (AI) has become a pervasive technology to design systems having the ability to function in a smart way such as self-adaptivity and on-the-fly performance optimization following the operation conditions. A primary limitation is the the need for massive data centers, such cloud solutions, and centralized architectures as well as the need to move data to algorithms which makes AI systems dependent on the cloud connectivity by which security threats and privacy concerns raise.

Embedded AI project will revert the current AI processing flow from collecting data at the edge and processing it at the cloud, to a flow where AI algorithms are migrated from the cloud to a distributed network of AI enabled edge-devices, which will increase responsiveness and functionality, reduced data transfer, and increased resilience, security, and privacy. This transformation is enabled by the merging of AI and IoT into “Artificial Intelligence of Things” (AIoT), and has created an emerging sector of Embedded AI (eAI), where all or parts of the AI processing are done at the edge level. In essence, this will enable AI to perceive and learn in real-time by mirroring critical AI functions across multiple disparate systems, platforms, sensors, and devices operating at the edge.

The project will be driven using a pilot case study “Triton embedded controller for ship systems” from MAN ES with a focus on predictive maintenance. The overall system comprises different sensors, mechanical components and a software (feedback-loop) control deployed on the Triton hardware. The ultimate goal is to monitor the operation conditions and learn how components (injection pumps, hydraulic system, etc) deteriorate over time so that maintenance tasks can be planned prior to components wearing. Adopting embedded AI and ML to achieve predictive maintenance will lead to reduce the maintenance cost and time for MAN ES and its customers. Digital twins will be a key technology to achieve the project.

About the Employer: Aarhus University (AU)

Note or Other details

All interested candidates are encouraged to apply, regardless of their personal background. Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants.


ADVERTISEMENT
Scroll Down for Content

Contact details

Applicants seeking further information are invited to contact:

Advertisement Details: Embedded Artificial Intelligence

Other Vacancies from this field

ADVERTISEMENT
Scroll Down for Content


Support us by Sharing!