Alfonso Gijón

Short Bio

Since 2024, I have been an Assistant Professor with the Department of Mathematics at the Universidad de Córdoba (Spain). Prior to my current role, I worked as a postdoctoral researcher at the Universities of Granada and Seville, and completed a research stay at Imperial College London. I earned my PhD in Physics from the Universidad Autónoma de Madrid in 2021, specializing in the modeling and simulation of atomic and molecular systems at the Madrid Institute of Materials Science (ICMM-CSIC).

My research interests lie at the intersection of physical simulations, numerical methods, and artificial intelligence. During my predoctoral period, I acquired solid experience in atomistic simulations, including Monte Carlo methods, classical molecular dynamics, and path-integral quantum molecular dynamics. Recently, my work has focused on developing hybrid methodologies that integrate machine learning techniques with traditional numerical methods. Lately, I have been exploring the application of physics-informed neural networks (PINNs) and graph neural networks (GNNs) to solve PDEs and regression tasks across various scientific fields. The versatile nature of these approaches has allowed me to build a highly interdisciplinary profile, actively collaborating with research groups in mathematics, physics, chemistry, engineering, and medicine to address pressing challenges in applied mathematics, materials science, energy efficiency, and healthcare.

In my spare time, I love traveling, exploring nature (camping, trekking...), and practicing outdoor sports like cycling.

Publications

Teaching

Throughout my experience as a university lecturer, I have taught various courses in Physics, Mathematics, and Programming at the Universidad Autónoma de Madrid (UAM), the Universidad de Granada (UGR), and the Universidad de Córdoba (UCO).

If you would like to schedule an office hour or a tutoring session, please feel free to email me here .

Supervision

Here you can find a list of the Bachelor's (TFG) and Master's (TFM) theses I have supervised. If you are looking for an advisor and are interested in my research areas or would like to collaborate, please feel free to reach out so we can find a project of mutual interest.

Trabajos de Fin de Grado (TFG)

1. Inteligencia artificial para predicción de propiedades moleculares.
   Alumno: José Luis Antiñolo Álvarez  |  Cotutor: Rafael G. Uceda  |  Grado de Física  |  Curso: 2025/2026
2. Introducción a simulaciones de dinámica molecular con LAMMPS.
   Alumna: Vanessa Aguilera Roldán  |  Grado de Física  |  Curso: 2025/2026
3. Predicción de propiedades moleculares con redes neuronales.
   Alumno: Alejandro Nadal López-Cepero  |  Grado de Física  |  Curso: 2024/2025
4. Uso de redes neuronales con información física para resolución de ecuaciones diferenciales y estimación de parámetros físicos.
   Alumno: Álex Vadillo Sánchez  |  Grado de Física  |  Curso: 2024/2025
5. Formalismo Path Integral para simulación cuántica de sistemas atómicos y moleculares.
   Alumno: Alejandro Lara Huete  |  Cotutor: Antonio J. Sarsa Rubio  |  Grado de Física  |  Curso: 2024/2025
6. Aplicación de algoritmos cuánticos variacionales a la resolución numérica de la ecuación de Schrödinger para sistemas de uno y varios cuerpos.
   Alumno: Daniel Pérez García  |  Cotutor: Antonio J. Sarsa Rubio  |  Grado de Física  |  Curso: 2024/2025

Trabajos de Fin de Máster (TFM)

1. Aceleración computacional de EnergyPlus mediante modelado subrogado: fundamentos para el control por aprendizaje por refuerzo.
   Alumno: Alejandro Nadal López-Cepero  |  Cotutor: David Guijo  |  Máster en Inteligencia Computacional e Internet de las Cosas  |  Curso: 2025/2026
2. Aprendizaje Profundo con Grafos para Simulación de Sistemas Físicos.
   Alumno: Zewei Wang Chen  |  Cotutor: Juan Gómez Romero  |  Máster Universitario en Ciencia de Datos e Ingeniería de Computadores  |  Curso: 2021/2022

Contact

If you would like to collaborate, have any inquiries, or wish to propose a project, please feel free to get in touch: