Job offer

Find gene features with a feature filtering procedure to deal with the large feature set necessary to predict the thermoelectric ZT of a material.

• Improve the already existing experimental dataset.

• Apply different machine learning techniques (RF, XGBoost, NN, SISSO) to screen all the possible compositions of the half-Heusler family in order to find high ZT materials.

• Propose new ML methods.

• Perform DFT calculations to compute the predicted ZTs via first principles calculations.

• Computer simulations: ML + DFT

• Scripting (Python)

• Analysis of the results + writing publications

• The position is part of an ANR-DFG project, combining theoretical and experimental work.

• The work will take place in the department of theoretical chemistry (D5) of the ICG in Montpellier.

PhD or equivalent

Research Field Chemistry

Education Level PhD or equivalent

Languages FRENCH

Level Basic

Research Field Chemistry » Physical chemistry

Years of Research Experience 1 - 4

Research Field Chemistry » Computational chemistry

Years of Research Experience 1 - 4

Additional Information

Eligibility criteria

• A strong knowledge in computer science (ML, DFT, Python, visualization)
• A strong knowledge in materials science (transport properties, crystallography, electronic properties)