PhD Position - Applied Artificial Intelligence for Aeronautical Meteorology

Is the Job related to staff position within a Research Infrastructure?: No

Offer Description

A call for applications is open for 1 Research Grant within the scope of the ARIES project, reference M2030-FEDER-03053400, funded by IDR – Instituto de Desenvolvimento Regional, under the following conditions:

I. Scientific Area: Applied Artificial Intelligence or “related areas”.

II. Admission requirements:

a) General requirements:

a. Enrollment in a doctoral program or a non-degree course. The non-degree course must be developed in association or cooperation between the higher education institution and one or more R&D units.

b. Holding a Master's degree in an area relevant to the project.

c. Good knowledge of the English language.

b) Preferred requirements:

a. High motivation for applied research in aeronautical meteorology, artificial intelligence and operational decision support.

b. Proactivity, autonomy, responsibility and initiative in developing solutions to complex scientific and operational problems.

c. Critical analysis skills, problem-solving abilities, and interpretation of meteorological phenomena associated with low visibility, fog, and low clouds.

d. Ease of interpersonal relationships, multidisciplinary collaboration, and teamwork.

e. Good scientific writing skills, technical communication, and dissemination of results.

f. Creativity, resilience, persistence, and methodological rigor in the development and validation of predictive models.

III. Work Plan:

1. Systematic review of the state of the art on forecasting reduced visibility, fog, mist, low clouds, and applications of artificial intelligence in aeronautical meteorology.
2. Collection, organization, and pre-processing of meteorological, aeronautical, and operational databases relevant to the study of low visibility phenomena and low clouds in an airport environment.
3. Exploratory analysis of atmospheric patterns associated with the formation, persistence, and dissipation of fog, mist, and low clouds in an airport environment.
4. Development of machine learning and deep learning models for forecasting reduced visibility, low cloud ceilings, and adverse weather conditions impacting air operations.
5. Implementation of explainability and interpretability methodologies applied to the developed models, with the aim of understanding the most relevant meteorological factors for the occurrence of low visibility events.
6. Experimental validation of the models using statistical metrics, comparison with traditional forecasting methods, and performance evaluation in different airport operational scenarios.
7. Development of nowcasting prototypes to support decision-making in airport operations affected by fog, mist, low clouds, and reduced operational ceilings.
8. Writing scientific articles, disseminating results, and preparing the doctoral thesis.

Objectives

1. To develop artificial intelligence models for forecasting low visibility and low clouds at airports located in meteorologically complex environments.
2. Investigate the atmospheric processes associated with the formation, intensification, and dissipation of fog, mist, and low cloud ceilings, focusing on safety and operational efficiency.
3. Develop and deepen deep learning methodologies, explainable neural networks, and hybrid methods for nowcasting visibility and cloud ceiling in an aeronautical context.
4. Develop automatic decision support tools for risk mitigation in airport operations under low visibility conditions.
5. Produce interpretable and robust models for forecasting fog, mist, low clouds, and reduced visibility in an airport environment.

IV. Applicable legislation and regulations: Statute of the Scientific Research Fellow in its current wording; Research Grant Regulations of the Foundation for Science and Technology I.P.; Research Grant Regulations of the University of Madeira.

V. Workplace: The work will be carried out at the Faculty of Exact Sciences and Engineering of the University of Madeira, under the scientific guidance of Décio Alves, Fábio Mendonça and Morgado Dias.

VI. Duration of the grant(s): The grant will last for 6 months, and may be renewed for equal periods, until the end of the project in 2029.

VII. Amount of the monthly maintenance allowance: The grant amount corresponds to €1,359.64, according to the table of values for grants awarded directly by FCT, I.P. in Portugal (http://alfa.fct.mctes.pt/apoios/bolsas/valores).

VIII. Funding: The grant awarded under this competition will be funded by the European Regional Development Fund (ERDF), through the Madeira Regional Program 2021-2027 (Madeira 2030), by Portugal 2030 and by the European Union. Operation Code: M2030-FEDER-03053400.

IX. Selection Methods: The selection methods to be used will be as follows: curriculum evaluation and interview, with respective weightings of 50% and 50%.

X. Composition of the Selection Jury:

President of the Jury: Prof. Dr. Fernando Manuel Rosmaninho Morgado Ferrão Dias

Member: Prof. Dr. Fábio Rúben Silva Mendonça

Member: Prof. Décio Damasceno Mendonça Alves

Alternate Member: Prof. Dr. Diogo Nuno Teixeira Freitas

XI. Method of publication/notification of results:

The admission lists, as well as the results of each selection method and final ranking lists, will be published on the website of the Human Resources Unit – Scholarship Holder Center of the University of Madeira, http://urh.uma.pt/, with the candidate being notified by email.

XII. Application deadline and method of submission:

a) Applications may be submitted within 15 working days following the publication of the Notice;

b) Applications must be formalized by sending an application letter accompanied by the following documents:

• Letter of motivation;
• Updated Curriculum Vitae;
• Copy of academic qualifications certificate;
• Proof of enrollment in a doctoral program or non-degree course.

c) Applications must be submitted in person between 9:00 am and 12:30 pm and between 2:00 pm and 5:30 pm at the Human Resources Unit – Scholarship Holder's Office of the University of Madeira, located at Colégio dos Jesuítas, Rua dos Ferreiros, 9000-081 Funchal, or by mail to the same office and address, or by email to nucleo.bolseiro@mail.uma.pt addressed to the Jury President, with the scholarship reference in the subject line.

Where to apply

E-mail: nucleo.bolseiro@mail.uma.pt

Requirements

• Research Field: Other
• Education Level: Master Degree or equivalent

Additional Information

Work Location(s)

• Number of offers available: 1
• Company/Institute: Universidade da Madeira
• Country: Portugal

Contact

• State/Province: Madeira
• City: Funchal
• Website: https://www.uma.pt/sobre/servicos-administrativos/unidade-de-recursos-humanos-urh/bolsas/
• Street: Colégio dos Jesuítas, Rua dos Ferreiros
• Postal Code: 9000-082
• E-Mail: nucleo.bolseiro@mail.uma.pt