Artificial intelligence tools to recognizing frequentation in natural spaces.
The International Centre for Numerical Methods in Engineering (CIMNE) is a research centre, created in 1987 by consortium between the Catalan Government and the Universitat Politècnica de Catalunya (UPC-BarcelonaTech), devoted to the development and application of numerical methods to a wide range of areas in engineering. CIMNE has been selected as a Severo Ochoa Centre of Excellence for the period 2019-2023. This is the highest level of recognition of excellence and leadership awarded to a research centre in Spain.
Pedro Arnau and Javier Mora
Number of vacancies: 1
Category: PhD (PHD2)
Salary (gross): 17.563,14 EUR
Weekly working hours: Full time
Duration: 3 years
Starting date: No later than Sept 2021
CIMNE is looking for a PhD Researcher to be part of the Research and Technical Development (RTD) Group on Information and Communication Technologies.
The functions assigned to the candidate will be:
Motivation: The main objective of the PhD work is the research and development of a tool to improve knowledge about frequentation in natural spaces.
The Ph.D. work will focus on the development of two systems: (a) a system for recognizing frequentation in natural spaces with artificial intelligence (AI) and Deep Learning (DL) that allows the monitoring and registration of the frequentation based on images, sound records and IoT devices and (b) an early warning system, embedded in the same IoT devices, that allows two-way communication between users and managing body, using the mobile screen as the screen that any citizen carries in their pocket, but without having to download any phone application. This early warning system must include a Machine Learning (ML) tools to, depending on the environmental variables and pre-existing frequentation, modify the message of the communications to the users.
This Ph.D. work will be based on concepts gained from previous CIMNE frameworks developed in the framework of Hamelin project, and include software developed in IDL and compilation of varied data sources in a single GIS, computational models’ repository.
Expected outcomes are the following:
Other possible areas of application of the technology developed could be the protection of the terrestrial and marine environment, the sustainable management of the territory, and in particular of natural resources, the maintenance of biodiversity and ecosystems, the sustainable management of agriculture, aquaculture, livestock, forest resources, integrated water management and technologies aimed at the efficiency of use in irrigation, rural, urban and industrial environments, the fight against desertification, the management of forest fires or the study of erosion.
C. S. Jacques, Jr., S. R. Musse, and C. R. Jung, “Crowd analysis using computer vision techniques,” IEEE Signal Process. Mag., vol. 27, no. 5, pp. 66–77, Sep. 2010.
Arnau P., Oñate E., Jiménez J. and Piazzese J., Development and application of decision support systems for environmental monitoring, MAMERN11: 4th Int. Conf. on Approximation Methods and Numerical Modelling in Environment and Natural Resources, Saidia, Morocco, May 23-26, 2011
A. Guillén-Pérez and M. D. C. Baños, “A wifi-based method to count and locate pedestrians in urban traffic scenarios,” in 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2018, pp. 123–130
The requirements and merits will be evaluated with a maximum mark of 100 points. Such maximum mark will be obtained by adding up the points obtained in the following items:
Candidates must complete the "Application Form" form on our website, indicating the reference of the vacancy and attaching the following documents in English:
The deadline for registration to the offer ends on 31st May, 2021 at 12 noon.
The shortlisted candidates may be called for an interview. They may also be required to provide further supporting documentation.
CIMNE is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law. CIMNE has been awarded the HRS4R label.