---
title: "PhD in AI for predictive maintenance in water and electricity infrastructure"
date: last-modified
editor: visual
---

### Short Description

Are you interested in building deep learning solutions to achieve real-time-based predictive maintenance algorithms for real-world applications? In this project, you will be exploring unsupervised and self-supervised learning methods. Moreover, you will have the possibility to test the results of your work with water and energy providers. This position is part of a collaboration between several industrial and academic partners.

![Maintenence](img/Maintenance_work_in_the_refinery.webp "Okane nyatoh daniel, CC BY-SA 4.0 \<https://creativecommons.org/licenses/by-sa/4.0\>, via Wikimedia Commons")

### Job Description

Predictive maintenance offers great potential value to the energy and water supply industry. Timely detection of required maintenance of machines, sensors, or other critical infrastructure can prevent service disruptions and costly loss of resources. For instance, visual sensors can be used to analyze and detect subtle patterns, and auditory sensors can pick up subtle sound changes.

Artificial intelligence allows for improved prediction performance on predictive maintenance tasks. However, one of the main challenges is dealing with real-time-based predictive maintenance. Instead of treating predictive maintenance as a simple alert monitoring, real-time-based predictive maintenance offers an estimate of time-to-failure. You will be addressing this challenge by working with large amounts of unlabeled visual and auditory datasets to create models for continuous and reliable monitoring of system states that can accurately predict time-to-failure.

You will work on deep learning solutions to achieve real-time-based predictive maintenance algorithms. These models will be based on unsupervised and self-supervised learning, and you will work with both discriminative and generative models. Moreover, you will study the accuracy, reliability, and possible integration of these models with water and energy providers and other industrial partners.

Your work will contribute to achieving a more reliable distribution of energy and water and to the more general goal of building robust machine learning for a sustainable society.

You will be part of the Jheronimus Academy of Data Science in 's-Hertogenbosch and closely collaborate with Tilburg University and the Eindhoven University of Technology.

### Job Requirements

We are looking for candidates that meet the following criteria: - Master's degree in a relevant field - e.g. Artificial Intelligence, Computer Science, Engineering, Cognitive Science. - Good knowledge of the theory and application of machine learning, particularly deep learning - demonstrable experience with self-supervised learning or deep generative models is a plus. - Well-developed programming skills - experience with deep learning frameworks is expected. - Good communicative skills in English, both in speaking and in writing (C1 level).

### Conditions of Employment

Being appointed at JADS provides a meaningful job in a dynamic and ambitious community of two universities and startups.

As a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.

To develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.

Next to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).

JADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community.

All researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at [Tilburg University](https://www.tilburguniversity.edu/about/working) and Working at [Eindhoven University of Technology](https://www.tue.nl/en/working-at-tue/) for more information on their respective employment conditions.

### More Information

Do you recognize yourself in this profile and would you like to know more? Please contact [Dr. Juan Sebastian Olier](mailto:J.S.Olier@tilburguniversity.edu)) or [Prof. Dr. Eric Postma](mailto:E.O.Postma@jads.edu). For information about terms of employment, please contact [Marielle van Gerven, HR Advisor](mailto:m.a.a.v.gerven@jads.nl) or [call](tel:+31402473699)

### Application

We invite you to submit a complete application by using the 'apply now'-button on this page. The application should include a:

-   Cover letter in which you describe your motivation and qualifications for the position.
-   Curriculum vitae, including a list of your publications and the contact information of three references.
-   Brief description of your MSc thesis.

We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.

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