PhD position on Electrochemical Battery Materials modeling

Updated: 3 months ago
Job Type: FullTime
Deadline: 14 Sep 2023

7 Sep 2023
Job Information

Politecnico di Torino

Energy Department
Research Field

Engineering » Computer engineering
Engineering » Materials engineering
Researcher Profile

First Stage Researcher (R1)

Application Deadline

14 Sep 2023 - 12:00 (Europe/Rome)
Type of Contract

Job Status

Is the job funded through the EU Research Framework Programme?

Is the Job related to staff position within a Research Infrastructure?


Offer Description

We are seeking a highly motivated PhD student to join our research team in the field of energy materials modeling. The successful candidate will work on the development of digital twins for describing transport and reactive phenomena within materials for energy storage in electrochemical batteries (e.g., Li-ion batteries), with the final aim to deepening our current understanding on the irreversible phenomena occurring within electrochemical cells leading to degradation processes and capacity fade. The project is funded by the Italian Ministry of Research and will be carried out at Politecnico di Torino (i.e. Multi-Scale Modeling Laboratory – SmaLL: ), at the Italian Metrological Institute – INRIM and in collaboration with other leading research groups both in Italy and Europe.


  • Develop and implement state-of-the-art atomistic models including machine learning potential based algorithms to study electrochemical battery materials;
  • Build digital twin models to describe transport and reactive phenomena within materials for energy storage in electrochemical batteries;
  • Utilize data from atomistic simulations (reactive molecular dynamics based on machine learning ML force fields, classical molecular dynamics, mesoscopic models such as kMC), experimental data extracted from literature, and generated through accurate metrological characterization (e.g., through Atomic Force Microscopy or Transmission Electron Microscopy);
  • Use computational tools based on molecular dynamics and artificial intelligence algorithms to produce accurate and multiscale atomistic models of electrode-electrolyte interfaces;
  • Analyze and interpret simulation results to gain insights into the mechanisms governing battery performance;
  • Collaborate with experimental researchers to validate simulation results and guide the design of new battery materials.

We offer:

  • A challenging and exciting research project in the field of energy materials.
  • Access to state-of-the-art computational and characterization resources.
  • Opportunities to collaborate with leading research groups in Europe.
  • A supportive and international research environment.
  • A competitive salary.

How to apply:

To apply, please submit the following documents:

  • A cover letter explaining your motivation and qualifications for the position.
  • A CV detailing your education, research experience, and publications (if any).
  • Contact information for at least two references.

Applications should be sent by e-mail to both Eliodoro Chiavazzo ( ) and Paolo De Angelis ( ) preferably within September the 14th at the latest with the following subject: “PhD Position on Digital Twins for Batteries”. In any case, applications will be considered until the position is filled. We encourage applications from all qualified candidates regardless of their gender, race, ethnicity, disability, sexual orientation, or religion.

Research Field
Engineering » Materials engineering
Education Level
Master Degree or equivalent

Research Field
Engineering » Mechanical engineering
Education Level
Master Degree or equivalent

Research Field
Engineering » Industrial engineering
Education Level
Master Degree or equivalent



  • Master's degree in energy engineering, materials science, physics, chemistry, or a related field.
  • Skills in numerical simulation and programming, computational methods and experience with atomistic modelling techniques are strongly desired;
  • Familiarity with electrochemistry and battery materials, as well as machine learning tools is desirable although not essential;
  • Excellent communication and teamwork skills;
  • Strong motivation and attitude towards research.


Research Field
Computer science » Modelling toolsEngineering » Mechanical engineering

Additional Information
Work Location(s)
Number of offers available
Politecnico di Torino

Where to apply



Corso Duca degli Abruzzi, 24 10129 Turin - ITALY
Postal Code


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