PhD: Theoretical & Computational Nanocrystal Growth

1000scholars; Zürich; On-site; CHF 60,000 - 76,000; Full time

Benefits offered by this job

Training in modeling

External research collaboration

Access to HPC

Interaction with experimentalists

International conferences

Student supervision

Job summary

ETH Zurich invites applications for a full-time doctoral position to develop theoretical and computational models of semiconductor nanocrystal growth. You will combine DFT energies with rate-equation models and kinetic Monte Carlo simulations to understand nanoplatelets, magic-sized nanocrystals, and quantum dots. The project is supervised by Prof.

David J. Norris in the Department of Mechanical and Process Engineering and coordinated with experimental groups; starting Oct 2026 or later,

Qualifications

• MSc degree in a closely related discipline.
• Good background in thermodynamics, kinetics, and solid-state physics.
• Ability to communicate fluently in English; independent work and collaboration.

Responsibilities

• Develop theoretical/computational descriptions of nucleation and growth of semiconductor nanocrystals.
• Construct mass-balance models for coupled growth/dissolution of nanocrystal populations.
• Perform kinetic Monte Carlo simulations to study early growth stages and morphologies.
• Collaborate with experimentalists and guide design of new experiments.

Skills

DFT

Kinetic Monte Carlo

Numerical rate equations

Atomistic modeling

Semiconductor nanocrystals

Scientific programming

High-performance computing

Education

MSc in chemistry, chemical engineering, mechanical engineering, materials science, physics, computational science

Tools

DFT software

Job description

ETH Zurich invites applications for a full-time doctoral position to develop theoretical and computational models of semiconductor nanocrystal growth. You will combine DFT energies with rate-equation models and kinetic Monte Carlo simulations to understand nanoplatelets, magic-sized nanocrystals, and quantum dots. The project is supervised by Prof.

David J. Norris in the Department of Mechanical and Process Engineering and coordinated with experimental groups; starting Oct 2026 or later,