ORGANISATION/COMPANYUniversité Gustave Eiffel
RESEARCH FIELDComputer science › Database managementComputer science › Modelling toolsEngineering › Civil engineeringEngineering › Mechanical engineeringMathematics › Computational mathematics
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE21/03/2022 17:00 - Europe/Brussels
LOCATIONFrance › Bouguenais
TYPE OF CONTRACTTemporary
HOURS PER WEEK35
OFFER STARTING DATE01/10/2022
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions COFUND
MARIE CURIE GRANT AGREEMENT NUMBER101034248
The skid resistance of the runways is essential to ensure the runway safety. Skid resistance, characterized by a friction coefficient, changes over time due to traffic and weather conditions. Predicting this evolution would allow airport managers to optimize the selection of material prior to runway construction and to plan runway maintenance work.
The evolution of skid resistance depends on many factors (pavement, traffic, meteorology, etc.) whose interactions cannot always be considered by existing models based on analytical or numerical approaches. Over time, friction coefficient measurements generate data that can be used to develop predictive models. One of the means that has proven to be adequate in the study of complex phenomena are Machine Learning techniques, among which Artificial Neural Networks – (ANN) stand out. ANN are techniques inspired by the functioning of the human brain. Moreover, these techniques are particularly suitable when it is necessary to exploit a large amount of data.
The monitoring of runway skid resistance through friction coefficient measurements generates, over the years, a large database including, for a given airport, various runway surfaces, various weather conditions, etc. Applying ANN techniques to this database to identify patterns of skid resistance evolution seems to be a promising approach.
The objective of this thesis is to develop a model for predicting the evolution of the tire / runway friction coefficient using machine learning techniques. For the development of a prediction model for the evolution of the friction coefficient, in addition to the in-situ measurement data, runway related variables can be used, such as the age of the pavement, the number of take-offs by aircraft type, the weather conditions (runway and surface temperature, precipitation and relative humidity), etc. Other variables that are not listed above, such as maintenance operations, can also be considered.
The data to be used will be obtained from technical reports provided by the Superintendency of Airport Infrastructure from the National Civil Aviation Agency (SIA/ANAC) in Brazil. It consists of data reports from 2015 to 2019, involving 32 airports. From this information, a database will be built. Then, a first selection will be made to verify which of them are related to the friction coefficient. This verification can be done using a correlation matrix, for example.
The, after selecting the variables, the data will be divided into training, testing and validation sets. Each of these sets has a role in the development of the model, the first set is used to train the model itself, the second set is used to test after training and make adjustments, and finally, the third set validates the model with data that was not used in the first two sets.
Another important step will be the application of data preprocessing. This procedure is common for machine learning algorithms in order to avoid overfitting the model; because the objective is to obtain a model capable of generalizing satisfactorily to data beyond those used in its learning, its tests and its validation.
Finally, the developed model will be compared to previous models such as those of the AME/EASE laboratory. The objective of this comparison is to identify the complementarity between the physical and statistical approaches: the evolutions resulting from the statistical approach - machine learning - require an interpretation based on the physical approach. Prospects for integrating the evolution of skid resistance into runway maintenance management tools will also be discussed.
- High-quality doctoral training rewarded by a PhD degree, delivered by Université Gustave Eiffel
- Access to cutting-edge infrastructures for research & innovation.
- Appointment for a period of 36 months based on a salary of 2 700 € (gross salary per month).
- Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year.
- International mobility will be mandatory
- An international environment supported by the adherence to the European Charter & Code.
- Access to dedicated CLEAR-Doc trainings with a strong interdisciplinary focus, together with a Career development Plan.
- At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree. At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
- At the time of the deadline, applicants must be in the first four years (full-time equivalent research experience) of their research career (career breaks excluded) and not yet been awarded a doctoral degree. Career breaks refer to periods of time where the candidate was not active in research, regardless of his/her employment status (sick leave, maternity leave etc). Short stays such as holidays and/or compulsory national service are not taken into account.
- At the time of the deadline, applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately prior to the call deadline.
- Applicants must be available to start the programme on schedule (around 1st October 2022).
- Please refer to the Guide for Applicants available on the CLEAR-Doc website.
- The First step before applying is contacting the PhD supervisor. You will not be able to apply without an acceptation letter from the PhD supervisor.
- Please contact the PhD supervisor for any additional detail on job offer.
- There are no restrictions concerning the age, gender or nationality of the candidates. Applicants with career breaks or variations in the chronological sequence of their career, with mobility experience or with interdisciplinary background or private sector experience are welcome to apply.
- Support service is available during every step of the application process by email: firstname.lastname@example.org
Web site for additional job details
REQUIRED LANGUAGESENGLISH: Excellent
- At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
- At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
This thesis will be in cosupervision with DET - Departamento De Engenharia De Transportes, Universidade Federal do Ceará (Brazil). For more information, contact the PhD thesis supervisor.
EURAXESS offer ID: 716436
The responsibility for the jobs published on this website, including the job description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.
Please contact email@example.com if you wish to download all jobs in XML.