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Job ID: 193423

Post-doc Edge Preprocessing for Machine Learning in the Cloud - REF 22PDR176
University de São Paulo

Date Posted Sep. 1, 2022
Title Post-doc Edge Preprocessing for Machine Learning in the Cloud - REF 22PDR176
University University de São Paulo More About This Institution...
Sao Paulo, Sao Paulo, Brazil
Department Mechatronics Engineering
Application Deadline Sep. 15, 2022
Position Start Date October 2022
  • Post-Doc
  • Mechatronics
    Engineering Physics
    Computer Engineering
    Engineering - Other

Research theme area

Data Science and Instrumentation to investigate Greenhouse Gases Behavior – Group E3



In this proposal, a combination of edge and cloud framework will be adopted, implemented and tested in the context of GHG emissions data collection and analysis. The critical issue of the proposed edge cloud architecture investigated in this project is that it should provide a discussion of the division of tasks that must be performed in the cloud and the tasks implemented close to the user or data source at the edge of the network.

The target processes of the study are trajectory planning algorithms for autonomous aerial exploration using robotic platforms. The proposed planner employs an optimization scheme with a decreasing horizon: in an online environment, the path to be taken must optimize the search, observing quantities of interest such as computed trails, temperature, pollution, deforestation, etc. to find the best route, the quality of which is determined by the amount of unmapped space that can be explored, taking into account the aircraft range restrictions. The proposed planner must be able to be processed online, aboard a UAV with limited resources. Its high performance is evaluated in detailed simulation studies as well as a challenging real-world experiment using a helicopter micro air vehicle. Analysis of the computational complexity of the algorithm must also be analyzed to verify the proposal's ability to deal with large-scale issues.

The system must take into account the capacity of data storage, communication and computational division of tasks with a base station and also with cloud storage and processing technology.



The development of the work can be described in the following steps:

  1. Literature Review: A. How GHG emission requirements can be measured by aircraft - range, mission, geometry, architecture, desired flight height, payload (sensors), robustness, etc. B. Candidate embedded technologies C. Candidate propulsion technologies.
  2. Virtual set design. Design of different candidate aircraft - computer modeling and implementation to perform simulations and performance tests
  3. Development of experiments and testing of subsets for feasibility analysis
  4. Design and implement division of tasks between embedded system, edge computer and cloud system
  5. Design, implement and test prototype of an embedded system connected to sensors of interest to GHG
  6. Design and implement an edge system prototype connected to sensors of interest to GHG.
  7. Prototyping cloud structure for receiving data streaming, organizing and processing


Requirements to fill the position:

This project is suitable for a highly motivated candidate and requires knowledge of aircraft design, autonomous systems, embedded systems, deep neural network, machine learning, C/C++ and Python computer languages. The candidate must have a PhD in Engineering or Doctoral student with impending defense.

The candidate must have obtained a doctorate degree less than seven years ago, priority for candidates who have just completed the Doctorate, within the regular duration, with an excellent academic record in postgraduate studies.


Funding Notes:
This Postdoc Fellowship is funded by FAPESP. The fellowship will cover a standard maintenance stipend of R$ 8.479,20 (reais) per month.


Position: Post-Doctoral REF: 22PDR176 AND APPLICATION AT REF 22PDR176 – Post Doctoral

Please reference in your cover letter when
applying for or inquiring about this job announcement.

Contact Information

  • RCGI
    Human Resources
    University De São Paulo
    Av Prof Mello Moraes, 2231
    Cidade Universitaria - Butanta
    Sao Paulo, Sao Paulo 05508-030


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