CALL FOR APPLICATIONS (Call ID: 63/2025)

Post/s of Part-Time Research Support Officer II

Projects funded by STMicroelectronics (Malta) Ltd:

Project 1: Smart Sampling - Find a methodology to optimise the inspection steps by selecting a good sample of products to be automatically inspected

Project 2: Smart Burn-In Maintenance - Optimise the Burn-In process to better perform maintenance operations. 

And any other projects undertaken by the Department of Artificial Intelligence, Faculty of Information and Communication Technology.

Applications are invited for a Research Support Officer (RSO) to carry out duties in relation to two main research projects, which are funded by STMicroelectronics (Malta) Ltd. The RSO may also be required to work on other projects undertaken by the Department of Artificial Intelligence, Faculty of Information and Communication Technology.

Applicants must be in possession of a Master’s degree related to Artificial Intelligence and Vision. 

The University of Malta is an Equal Opportunity employer.

The selected candidate must be living in Malta during the period of employment.

The post is for a period of 24 months and will entail an average of 26 hours of work per week.

The RSO is to be regularly present at the ST premises (estimated at 21 hours per week at ST; 5 hours per week at UM).

The initial remuneration per hour (inclusive of any cost-of-living adjustment) shall be €14.22. 

Candidates must upload their covering letter, curriculum vitae, and certificates (certificates should be submitted in English) and contact details of at least two referees through this form https://www.um.edu.mt/hrmd/workatum-projects by not later than Sunday, 20th April 2025. 

Late applications will not be considered. 

Further information may be obtained from http://www.um.edu.mt/hrmd/recruitment and should you have any queries, please send us an email on projects.hrmd@um.edu.mt

Further Information

Project 1: Smart Sampling - Find a methodology to optimise the inspection steps by selecting a good sample of products to be automatically inspected

During the manufacturing process, several inspection steps are performed to ensure the quality of the product ST will deliver to the customers. Those inspection steps are systematic for our products whatever happened in the previous manufacturing steps or previous inspections steps. Two kinds of inspection are done: Automated X Ray Inspection and Automated Optical Inspection. With a new smart sampling methodology, ST would like to improve those inspection steps and not make them systematic anymore. This would be possible by choosing a good sample of semi-conductors to be inspected. Indeed, by analysing the previous assembly steps before a given inspection, and the previous inspection in the manufacturing process, it should be possible to identify the products that are most likely to be defective and to inspect them only. The consequence of this would be to decrease the number of leads requested to be inspected and decrease the average time spent on inspection during the manufacturing process. 

Project 2: Smart Burn-In Maintenance - Optimise the Burn-In process to better perform maintenance operations. 

In the Burn-In process devices are loaded on Burn-In boards and then tested while being heated up in an oven. The performance of the Burn-In boards degenerates over time and devices will need to be retested to check if they failed due to a bad board or due to a problem with the device. Problematic Burn-In boards need to be replaced. The analysis to start maintenance procedures and replacement of these boards is still a manual process. The goal of the project is to identify the right automatization steps and to implement them to modernise this analytical process step. Promising first projects in this perspective are the maintenance/deactivation of test sockets and the ordering of new Burn-In boards. Currently new Burn-In boards are ordered in a reactive way when old Burn-In boards do not have the required performance anymore. It takes several weeks from ordering until the new boards arrive. This project will investigate the potential gain of proactively ordering new boards when the performance of the board is not yet deteriorated below an acceptable level. In case of a positive financial outcome, an AI prediction system will be developed. One aspect of the Burn-In board performance deterioration is failing sockets. These are failures within a socket which prevent it from successfully testing devices in every Burn-In cycle. Currently such sockets can either be fixed in a maintenance intervention or they are manually deactivated when they are observed. AI supported systems should be able to identify potentially failing sockets earlier and more reliably. If possible, the problematic sockets can be fixed, otherwise they can be deactivated to avoid future failed tests.

Further information may be obtained by contacting Prof. Matthew Montebello at matthew.montebello@um.edu.mt. The RSO may also be required to work on other projects undertaken by the Department of Artificial Intelligence, Faculty of Information and Communication Technology.

The appointee will be expected to undertake the following tasks:

1. properly document the solution and train ST Team in the application and usage of the developed solution;
2. contribute to small ST projects. The topics would be decided by ST, with the estimated time of commitment to approximately 80 hrs over each 6 month period during the Term, to be computed within the twenty-six hour work week;
3. be open to contacts with all levels at ST, and educate Data Scientists or other teams at ST, for the purpose of AI and machine learning;
4. deliver one talk per month at ST on such topic to be duly agreed with ST;
5. submit a near-final draft version of the materials to be presented, published, or disseminated to ST for review, at least 30 (thirty) days before submission.
6. perform any other project related task as instructed by the project coordinator and key experts.

The appointee will be expected to work at such places and during such hours as may be determined by the University authorities.

The selection procedure will involve:

6. scrutiny of qualifications and experience claimed and supported by testimonials and/or certificates (copies to be included with the application);
7. shortlisting; and
8. an interview and / or extended interview.

The post is for a period of 24 months, which will be subject to a probationary period and to the provisions of the Statutes, Regulations and Bye-Laws of the University of Malta which are now or which may hereafter be in force.

Office of the University, 

Msida, 5th April 2025