Research support technician 2025 / CP / 082

University of A Coruña; Jaén; Presencial; EUR 20.000 - 30.000; A tiempo parcial

Descripción de la vacante

La Universidad de A Coruña busca investigadores en Ingeniería para desarrollar técnicas avanzadas en análisis de datos astronómicos usando herramientas de inteligencia artificial. En este puesto a tiempo parcial, se realizarán investigaciones innovadoras en el campo de la astronomía, enfocándose en la clasificación y validación de datos observacionales. Se requiere un grado en informática y experiencia relevante, ofreciendo un ambiente académico estimulante.

Formación

• Grado en ingeniería informática y/o máster relacionado preferido.
• Conocimientos en programación y análisis de datos son esenciales.
• Experiencia previa en proyectos de investigación y publicaciones será valorada.

Responsabilidades

• Desarrollar herramientas de validación de clasificación no supervisada de objetos astronómicos.
• Desarrollar técnicas de aprendizaje profundo para la reducción de dimensionalidad.
• Apoyar en la generación de documentación del proyecto.

Conocimientos

Análisis de datos

Deep Learning

Clasificación

Estadísticas bayesianas

Educación

Grado en ingeniería informática

Máster relacionado con el campo

Descripción del empleo

Organisation / Company : University of A Coruña

Research Field : Engineering >

Researcher Profile : First Stage Researcher (R1)

Positions : Bachelor Positions

Country : Spain

Application Deadline : 21 May 2025 - 15 : 00 (Europe / Brussels)

Type of Contract : Permanent

Job Status : Part-time

Offer Starting Date : 1 Sep 2025

Funding : Not funded by an EU programme

Reference Number : 2025 / CP / 082

Research Title : AXUDAS PARA A CONSOLIDACIÓN E ESTRUTURACIÓN DE UNIDADES DE INVESTIGACIÓN COMPETITIVAS. GPC

Offer Description

Research line : Computer Science and Information Technology

Location & Schedule : CITIC - Monday to Friday : 10 : 30 - 14 : 00h

Tasks to Perform

• Development of tools for validation of unsupervised classification of astronomical objects observed by Gaia within the DPAC consortium and CU8 unit.
• Validation of star parameterization results using Gaia RVS data, including external validation with astronomical sources and internal validation considering observational and neural network errors, as well as quantification of confidence intervals and studies of objects with anomalous chemical abundances.
• Development of deep learning techniques for reducing data dimensionality to enhance star parameterization.
• Development of generative deep learning methods for classifying non-parameterized sources and improving existing star data.
• Estimation of errors in distances derived from parallaxes using Bayesian statistics.
• Support in generating documentation for the project.

1. Academic record : Degree in computer engineering (1 point), master's related to the field (1 point).

2. Knowledge of English : B2=0.25 points; C1=0.5 points; C2=1 point.

3. Relevant work / research experience : including scholarships, research projects, publications, contracts, patents, and software registrations.

4. Experience with Gaia data analysis, especially spectra processing and high-resolution stellar spectra analysis.

5. Internationalization activities : participation in scientific meetings, research stays, training schools, dissemination activities.

6. Specific experience in AI, deep learning, classification, and clustering in Big Data Astronomy.

Note : The position may be vacated if no candidate scores above 6 points.

Selection Process

Apply via UDC online services, addressing applications to the Office of the Vice-Rectorate for Research and Transference, including the call reference. Deadline : 5 working days after publication.

Additional Requirements

Documents needed : ID, degree certificate, CV, declarations of eligibility and data veracity. For international candidates, contact for instructions.

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