Research Assistant

Universidad Carlos III de Madrid; Madrid; Presencial; EUR 30.000 - 50.000; A tiempo parcial

Descripción de la vacante

A leading university in Madrid seeks a Research Assistant to work on AI methods for aeroacoustics. The role involves analyzing noise reduction in aircraft design, utilizing data-driven techniques to identify flow structures associated with noise. Candidates should have a strong background in relevant fields and excellent communication skills. This part-time position offers a flexible working environment and a competitive salary.

Servicios

Flexible working environment

Collaborative team

10 months contract

Monthly salary of 1000–1200€

Formación

• Strong background in Statistics, Fluid Dynamics, Acoustics, and Signal Theory.
• Outstanding academic record; critical and creative thinking.
• Good proficiency in English (oral and written).

Responsabilidades

• Identify representative patterns in point pressure measurements.
• Assign state labels to temporal snapshots of 2D vector fields.
• Correlate point pressure measurements and jet flow fields.

Conocimientos

Statistics

Fluid Dynamics

Acoustics

Signal Theory

Team-working

Communication

Critical Thinking

Creative Thinking

Educación

BSc or MSc in Statistics

BSc or MSc in Aerospace Engineering

BSc or MSc in Mathematics

BSc or MSc in Physics

BSc or MSc in Telecommunication Engineering

BSc or MSc in Mechanical Engineering

BSc or MSc in Acoustic Engineering

Descripción del empleo

Universidad Carlos III de Madrid ( invites to fill the following Research Assistant position :

Ref. UC3M-INFLUENTIA-RESASSIST : AI methods for aeroacoustics

Description and objectives :

Addressing noise reduction stands as a paramount challenge in the design of future aircraft : 1.3 million of European citizens were exposed to more than 50 daily aircraft noise events above 70 dB during 2019 (1), increasing the risk of health issues. Aircraft noise abatement, in particular for turbofan engine noise, is required to address this issue. This project aims at deepening the knowledge in subsonic jet noise, i.e. the noise produced by the flow expelled by the engine, which represents one of the predominant noise sources. Subsonic jet noise is the result of the complex interplay between the more or less coherent flow structures covering the large wealth of turbulence scales which characterize the flow (2). The nonlinear dynamics resulting from this interplay is ultimately responsible for the noise emission through a non-trivial relation. The lower number of scales involved in the noise which is propagated away from the jet suggests that only a handful flow structures are relevant for noise : suppressing / altering them could result in an abatement of noise levels without impacting effectively on the propulsive performances of the engine. However, the complex relation existing between flow structures and noise emission makes it difficult to identify which should be the objective of flow control for noise reduction. The objective of this research is to study this relation and use data-driven AI-based methods to identify the flow structures associated to noise (and conversely the patterns in sound pressure associated with flow states). Flow states / structures will be extracted from velocity fields measured in jet experiments. Sound patterns / states will be extracted from the acoustic pressure measured in given location by microphone arrays.

Activities that will be carried out in the frame of this position will include :

Identification of representative patterns in point pressure measurements

The objective is to partition the space of observed signals into representative domains using dimension-reduction and clustering techniques (i.e., unsupervised learning) from the realm of Functional Data Analysis (FDA), time series, and data valued in metric spaces, including Functional Principal Components (FPCs) (3), t-Stochastic Neighbor Embedding (t-SNE) (4), Multi-Dimensional Scaling (MDS), dynamic principal components (5), and variations thereof.

Identification of velocity / pressure patterns in jet flow fields

The objective is assigning a state label to temporal snapshots of 2D vector fields of the jet flow measured by means of Particle Image Velocimetry using dimensionality-reduction techniques such as Spectral Proper Orthogonal Decomposition (SPOD) (6), Hilbert POD (7), and adaptations of t-SNE or MDS.

Correlation between both point pressure and jet flow fields

The objective is correlating point pressure measurements and jet flow fields exploiting recent advances in random forests in metric spaces (8-11), both from patterns determined in the previous objectives and from raw measurements.

1)European Aviation Environmental Report 2022, EASA.

2)Jordan, P. and Colonius, T. (2013). Wave packets and turbulent jet noise. Annu.Rev.Fluid Mech. , 45 : 173–195.

3)Ramsay, J.O. and Silverman, B.W. (2005). Functional Data Analysis . Springer, New York.

6)Schmidt, O. T. and Colonius, T. (2020). Guide to spectral proper orthogonal decomposition. AIAA J. , 58 (3) : 1023–1033.

7)Raiola, M. and Kriegseis, J. (2024). Jet noise line sources extraction from particle image velocimetry data using Hilbert proper orthogonal decomposition. In 30th AIAA / CEAS Aeroacoustics Conference (2024) (p. 3084).

8)Capitaine, L., Bigot, J., Thiébaut, R., and Genuer, R. (2024). Fréchet random forests for metric space valued regression with non Euclidean predictors. J. Mach. Learn. Res. , 25(355) : 1–41.

9)Qiu, R., Yu, Z., and Zhu, R. (2024). Random forest weighted local Fréchet regression with random objects. J. Mach. Learn. Res. , 25(107) : 1–69.

11) Serrano, D. and García-Portugués, E. (2025). Prediction balls for random forests with response in metric spaces. Preprint .

The successful candidate will work within the Aerospace Engineering Department – EAP Lab ( and the Department of Statistics – NICDA group ( of UC3M under the supervision of Dr. Marco Raiola and Dr. Eduardo García-Portugués.

Requirements and desirable profile :

• BSc or MSc holder in the following disciplines : Statistics, Aerospace Engineering, Mathematics, Physics, Telecommunication Engineering, Mechanical Engineering, Acoustic Engineering. Excellent candidates in other disciplines are also invited to apply.
• A strong background in Statistics, Fluid Dynamics, Acoustics and Signal Theory will be positively evaluated.
• Outstanding academic record; critical and creative thinking.
• Team-working and communications skills.
• Good proficiency in English (oral and written).
• Ability to deal independently and proactively with scientific and engineering challenges.

What we offer :

• 10 months part-time contract for 20 weekly hours.
• Monthly gross salary in the 1000–1200€ range.
• Become part of a young, dynamic, highly qualified, collaborative team.
• Flexible working environment and schedule.

How to apply :

Interested candidates must send their applications to indicating in the e-mail subject UC3M-INFLUENTIA-RESASSIST , including in a single pdf file :

• CV (max. 4 pages), including relevant professional experience and knowledge.
• Copy of diploma and grades from previous university studies.
• A motivation letter of experience, interests, and research goals (max. 1 page).
• The contact information for two references (will be contacted during the hiring process).

The research group aims to increase the share of women in academic positions, therefore applications from women are particularly encouraged.

Submission of applications is due by May 30th, 2025 (though early applications are strongly encouraged, and later applications will be considered until the vacancy is filled). The contract will begin in June 2025, though earlier / later start date can be agreed.

Seniority level

Seniority level

Internship

Employment type

Employment type

Full-time

Job function

Job function

Research, Analyst, and Information Technology

Higher Education

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