Call for applications | PhD position @ OpenCEMS Industrial Chair (deadline: June 25th, 2022)

Please, consider sharing this call for postdoc among last-year Master or Engineering students.


The Connected Environment & Distributed Energy Data Management Solutions (OpenCEMS) industrial chair addresses the issues that businesses and communities encounter when handling data management in their connected environments. The OpenCEMS research group aims at designing, implementing and deploying software solutions within Small and Large Scale Distributed/Connected Environments for better data collection/aggregation, information retrieval, and knowledge extraction.More details can be found here: OpenCEMS


Connected environments are typically defined as physical real world infrastructures hosting sensor networks that record phenomena from the real world. However, the increasing number of sensors in environments leads to a huge increase in data collection, making data retrieval from resources a big challenge. Resource indexing is the process of assigning each device an index. In other words, each device will have knowledge about its location and the zones of other sensors. This implies that sensors, that cannot store the entire environment’s information, will forward the query to another device or to the requested zone.  In addition, uncovered zone discovery is essential to reduce time and resource consumption considerably.  The global objective of this doctoral fellowship is the design and implementation of the following modules in the OpenCEMS platform: (i) Generate (centralized and distributed) resources index for a connected environment, (ii) Execute different types of queries (projection, selection, etc.) (iii) Implement different discovery protocols while considering resource/network capacities, and (iv) Deploy an experimental protocol to evaluate the proposed approaches.


  • The ideal candidate has a master degree in computer sciences
  • A previous experience in Indexing would be a plus
  • Outstanding analytical competence
  • Strong interest in research (Data Management, Machine learning, Information retrieval)
  • Excellent scripting and coding skills (in Python)
  • Excellent Communication skills
  • Autonomous and team working capabilities.


  • The Phd student will have the opportunity to work in a research group that gathers academic, and industrial partners. This environment allows the student to participate in research gatherings, conferences, and visiting/working in different environments (e.g., in a research lab, partner institutions, and companies).
  •  Duration: 36 months.
  • Gross Salary: 1870 euros / month (which includes extra gratification for teaching duties – 32h per year)
  • Main host institution: LIUPPA/OpenCEMS research group.


Please send your applications (in PDF format) to the following contact: [email protected]The application (written in English) should include:

  • A Curriculum Vitae (including your contact address, work experience, publications, software repositories)
  • A cover letter
  • Your Master degree grade transcripts and ranking
  • Two recommendation letters
  • Two of your best publications/implementations

Deadline for applications: June 25th, 2022.Start date: September 1st, 2022 (negotiable). Screening of applications starts immediately and will continue until a candidate is selected. Therefore, early applications are encouraged.


Candidates will first be selected based on their application file.Those selected after this first step, will then be interviewed.Application files will be evaluated based on the following criteria:

  • Grades and ranking during your Master degree, steadiness in your academic background
  • English language proficiency
  • Candidate’s ability to present her/his work and results

Work experience similar to an internship in a laboratory – or likewise; previously achieved research work (reports, publications).


Here are some recent papers that correspond with the scope of the research project:

  1. A. Sheth, “Internet of things to smart iot through semantic, cognitive, and perceptual computing,” IEEE Intelligent Systems, vol. 31, no. 2, pp. 108–112, 2016, doi: 10.1109/MIS.2016.34
  2. S. K. Datta, R. P. F. Da Costa and C. Bonnet, “Resource discovery in Internet of Things: Current trends and future standardization aspects,” 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015, pp. 542-547, doi: 10.1109/WF-IoT.2015.7389112
  3. Charith Perera and Athanasios V. Vasilakos. 2016. A knowledge-based resource discovery for Internet of Things. Know.-Based Syst. 109, C (October 2016), 122–136, doi: 10.1016/j.knosys.2016.06.030
  4. Z. Li, R. Chen, L. Liu and G. Min, “Dynamic Resource Discovery Based on Preference and Movement Pattern Similarity for Large-Scale Social Internet of Things,” in IEEE Internet of Things Journal, vol. 3, no. 4, pp. 581-589, Aug. 2016, doi: 10.1109/JIOT.2015.2451138 
  5. S. Abdelwahab, B. Hamdaoui, M. Guizani and T. Znati, “Cloud of Things for Sensing as a Service: Sensing Resource Discovery and Virtualization,” 2015 IEEE Global Communications Conference (GLOBECOM), 2015, pp. 1-7, doi: 10.1109/GLOCOM.2015.7417252
  6. S. K. Datta and C. Bonnet, “Search engine based resource discovery framework for Internet of Things,” 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), 2015, pp. 83-85, doi: 10.1109/GCCE.2015.7398707