[Hiring] Computational Materials Scientist @Subsense

Role Description

We are looking for a computational materials scientist who can help us evaluate novel nanoparticle core and core/shell designs using first-principles simulation. This role is best suited for someone who can work independently with a defined set of calculations, validate results carefully, and summarize findings clearly for an interdisciplinary R&D team.

Key Responsibilities

• Support first-principles simulation of magnetic and magnetoelectric nanoparticle materials, including candidate core materials beyond cobalt ferrite and selected core/shell design concepts.
• Help prioritize which nanoparticle designs are most promising for experimental follow-up.
• Example tasks may include:
• Set up and run DFT calculations for structural relaxation and SCF workflows.
• Run or support DFPT calculations where appropriate.
• Estimate and interpret materials response properties such as dielectric constants, elastic moduli, Born effective charges, piezoelectric coefficients, and related response tensors.
• Work with spin-polarized systems, magnetic ordering, magnetic moments, and magnetic material properties.
• Evaluate literature values and assess whether published material properties are reliable and reproducible.
• Manage calculations on HPC or cloud compute environments and troubleshoot convergence issues.
• Summarize results in clear written notes, including assumptions, input parameters, outputs, limitations, and recommended next steps.

Qualifications

• Hands-on experience with Quantum ESPRESSO or VASP.
• Experience with DFT workflows including relaxation, SCF, and convergence testing.
• Knowledge of spin-polarized calculations and magnetic materials.
• Proficiency in Python-based structure handling and post-processing, such as pymatgen, ASE, or similar tools.
• Familiarity with pseudopotential / PAW datasets and practical choices around functional, cutoff, k-point mesh, convergence, and validation.
• Experience with HPC job management using SLURM, PBS, or similar systems.

Requirements

• Experience with ferrites, spinels, perovskites, piezoelectric materials, magnetostrictive materials, or multiferroics (helpful but not required).
• Experience with DFPT calculations for dielectric, elastic, Born charge, or piezoelectric tensors (helpful but not required).
• Knowledge of magnetostriction, spin-orbit coupling, noncollinear magnetism, or magnetic anisotropy (helpful but not required).
• Experience with core/shell nanoparticle modeling or interface modeling (helpful but not required).
• Familiarity with cloud compute workflows (helpful but not required).

Engagement Model

This is a paid part-time contract or paid advanced internship, depending on experience level and availability. Work will be remote and flexible. The selected candidate will receive defined calculation goals and will be expected to return validated results with concise written interpretation.

Ideal Outcome

The goal is to help build a computational design workflow that can compare candidate nanoparticle materials, identify promising core and core/shell designs, and feed those candidates into a broader AI-guided nanoparticle ranking and prioritization framework.