Persian Gulf University was recognized by QNRF

Qatar National Research Fund is a foundation and platform for supporting and promoting research proposals in the state of Qatar. In addition, there is a notable capacity for building partnerships between the academic institutions inside of Qatar and abroad. Hopefully, the procedure of adding Persian Gulf University to the list of recognized universities at QNRF was completed. The researchers affiliated with Persian Gulf University can be involved in the proposals submitted jointly with joints in Qatar. The office of International Affairs at PGU hopes this can facilitate collaboration capacity building with academic institutions in Qatar as well as increasing the annual international grant of the university.

Call for Dual Certificate Post-Doc Positions in Asset positioning and Tracking System

The Office of International Affairs and Overseas Students at Persian Gulf University announced a call for hiring a joint dual certificate post doc researcher for a project on Distributed Edge-AI Vision-based Asset Positioning and Tracking System in collaboration between Persian Gulf University and Polytechnic university of Viana do Castelo (Portugal). The details of the call can be found as follows: Title Distributed Edge-AI Vision-based Asset Positioning and Tracking System Download the poster via this link Supervisors: Dr. Ahmad Keshavarz, PGU Dr. Sérgio Ivan Lopes , IPVC External Collaborator: Dr. Azin Moradbeikie,  CiTin – Centro de Interface Tecnológico Industrial Funding and Duration Duration: 1 to 2 years partially remote Funding: Monthly salary.  Completion Two Scopus indexed publications (one of which in JCR IF Journal) are required to complete the projects, and dual completion certificates will be issued from both side. Who can apply? PhD holders in Computer Engineering and Electrical Engineering familiar with deep learning can apply. Qualifications Good programming skills (Python), An outstanding research and publication track record, A solid knowledge of Applied Machine learning and Deep Learning    How to apply? The applicants can apply via email and send the required documents to (ICT@pgu.ac.ir) before the deadline. Please write ApplicantName_PostDoc(ICT5) as the subject of email. The strict closing date of the call is June 15, 2023 (Khordad 25, 1402). Required documents - Motivation Letter (one page; including the title and code of the post-doc position)- Recommendations from Supervisor(s)- CV- PhD and Master Transcripts- Competencies Certificates (Recommended)- Language proficiency proof (Recommended) More Description The next generation of industry is shifting towards collaborative autonomous systems that operate safely and efficiently in dynamic and unstructured environments. Key enablers for the implementation of Industry 5.0 include applications such as smart logistics, human-in-the-loop, digital twin, and real-time decision-making. Accurate localization and tracking of assets are becoming essential components for all of these enablers. For instance, a digital twin technology that creates virtual representations of physical assets must be continuously monitored and updated in real-time based on the corresponding asset's location and status to achieve optimal performance and support decision-making. Although Global Positioning Systems (GPS) have been widely used as a positioning technology in many application domains, its high cost and reduced accuracy in indoor environments have become significant limitations. To address these limitations, many researchers have justified the use of visual-based localization systems as a more cost-effective and accurate technology. Visual-based localization systems can offer higher accuracy than GPS, as they can utilize advanced image processing techniques to identify and track objects in real-time. With the increasing availability of high-resolution cameras and advancements in edge-AI technology, visual-based localization systems are becoming an increasingly attractive solution for indoor applications, including asset and operator identification and tracking. By utilizing edge computing, the system minimizes network load and latency, while distributed implementation enables seamless monitoring of multiple assets across different locations. This study is going to propose a distributed vision-based localization system that utilizes the power of edge-AI. As factories often have large areas, this system should provide real-time localization and tracking of assets using multiple cameras distributed throughout the area. The system is designed to identify objects and track their movements in real-time, while also minimizing network load and delay by processing data at the edge. The final goal of this study is to provide a secure and accurate localization system within a large-scale factory environment, while minimizing network load and latency.

Call for Iran-Denmark Joint Post-Doc: Smart Grid (Deadline Extended)

Title DIGITALLY ENABLED ASSET MAINTENANCE AND MANAGEMENT FOR SMART GRIDS Download the poster via this link The Office of International Affairs and Overseas Students at Persian Gulf University announces this call for hiring one joint dual certificate post doc researcher for a project on the digitally enabled asset maintenance and management for smart grids in collaboration between Persian Gulf University and University of Southern Denmark. The details of the call can be found as follows: Supervisors Dr. Hamid Reza Shaker, The University of Southern Denmark;Dr. Rahman Dashti, Persian Gulf University;Dr. Ahmad Keshavarz, Persian Gulf University; Funding and Duration Duration: 1 to 2 years remotely or partially remote Funding: Monthly salary.  Completion Two Scopus indexed publications (both of them which in JCR IF Journal) are required to complete the project, and two individual completion certificates will be issued from each side. Who can apply? PhD holders in Computer Engineering, Data Science, Electrical Engineering with artificial intelligence and smart grid background can apply. Qualifications Good programming skills e. g. Python and Matlab, An outstanding research and publication track record, A solid knowledge of Applied Machine learning and AI   How to apply? The applicants can apply via email and send the required documents to (ICT@pgu.ac.ir) before the deadline. Please write “ApplicantName_PostDoc” as the subject of email. The strict closing date of the call is March 1, 2023 (Esfand 10, 1401). Required documents - Motivation Letter (one page; including the title and code of the post-doc position) - Recommendations from Supervisor(s) - CV - PhD and Master Transcripts - Competencies Certificates (Recommended) - Language proficiency proof (Recommended) * * * More Description Introduction Digitalization has transformed the world’s power and energy systems and provided many opportunities to effectively address the emerging challenges regarding climate change and increased electrification. Digital data and analytics can help to reduce operations and maintenance costs, improve power plant and network efficiency, reduce unplanned outages and downtime, and extend assets’ lifetime. According to the International Energy Agency (IEA), the overall savings from these digitally-enabled measures would be around USD 80 billion per year over 2016-40. To realize these and enjoy the benefits of digitalization, it is important to develop and deploy new methods for digital asset maintenance and management for the electricity grids. Research Goal The goals of this postdoctoral research are to:Improve the current “wait until it breaks” approach to maintenance to a more proactive, predictive, and prescriptive maintenanceLeverage available digital data e. g. operation data, working environment data, etc to better prioritize asset replacement and plan renovation Background and/or Theories Today’s common practice in maintenance in electricity distribution grids is a reactive approach which is accompanied by expensive frequent preventive maintenance on some assets such as transformers and cable boxes. Therefore, the current maintenance practice relies heavily on manual inspection and does not use digital data. Many efforts have been dedicated to improving the current practice, e.g. through fault prediction and location [1]. These methods, although effective, they either not reliable or require data with a very high resolution. To the best of our knowledge, methods that can use the currently available low-resolution data for proactive, predictive, and prescriptive maintenance do not exist. Asset management is more advanced compared to maintenance still heavily relies on expensive manual inspections and assumptions and does not use operational conditions or environmental working conditions such as soil type, proximity to roads with damaging vibrations, etc.  These parameters are important to be considered in asset management as they affect the actual lifetime of the assets.  Methodology In this project, a digital twin will be developed for simulation-based prediction and analysis of faults and critical events in smart grids and the evaluation of possible solutions. This is expected to be carried out in MATLAB or PowerFactory environments. In addition, Machine Learning is expected to be used on the available time series to identify pre-failure symptoms and behavior, allowing us to predict faults at the early stages. Machine Learning will also be used for asset management to combine different available data on environmental working conditions and operation with grid information and to better plan asset replacement.

Call for Erasmus+ Mobility Winter 2023 from PGU to UCO

Regarding the renewed inter-institutional agreement between two institutions for Erasmus+ Mobility (2023) and the positive experience of mobility in 2022 from PGU to UCO, this call has been announced on behalf of the Office of International Affairs and Overseas Students to nominate the candidates for mobility from Persian Gulf University (PGU) to Universidad De Córdoba (UCO) for the available Positions in 2023. The details of the call can be found in the following link.https://pgu.ac.ir/en/news/1676