Computational Materials Scientist (Contract)

About Subsense

Subsense is a deep-tech startup working on a non-surgical, two-way brain-computer interface that uses plasmonic and magnetoelectric nanoparticles. The company’s purpose is to enable direct communication between the human brain and AI, beginning with medical use cases such as stroke rehabilitation and later expanding to cognitive enhancement for healthy users. Based in Palo Alto, Subsense combines senior scientists and engineers to push forward the future of human–machine interaction.

The Opportunity

Subsense is seeking a computational materials scientist to assess new nanoparticle core and core/shell architectures through first-principles simulation.

This position fits someone who can work with a clear set of calculations, independently verify outputs, and communicate conclusions in a structured way to a cross-functional R&D team.

Key Responsibilities

You will contribute to first-principles modeling of magnetic and magnetoelectric nanoparticle systems, including alternative core materials beyond cobalt ferrite and selected core/shell concepts. Your work will help determine which candidates are strongest for experimental validation.

• Prepare and execute DFT runs for structural relaxation and self-consistent field workflows.
• Carry out or assist with DFPT studies when they are relevant to the problem.
• Calculate and interpret response properties such as dielectric constants, elastic moduli, Born effective charges, piezoelectric coefficients, and related tensors.
• Work with spin-polarized systems, magnetic ordering, magnetic moments, and other magnetic characteristics.
• Review published data critically and judge whether reported material properties appear trustworthy and reproducible.
• Run calculations on HPC or cloud-based environments and resolve convergence or stability issues.
• Document findings in concise written notes, including assumptions, inputs, outputs, limitations, and suggested next steps.

Required Experience and Tools

Candidates should already have practical experience with the following:

• Quantum ESPRESSO or VASP.
• DFT workflows, including relaxation, SCF, and convergence testing.
• Spin-polarized calculations and magnetic materials.
• Python-based structure handling and post-processing tools such as pymatgen, ASE, or similar packages.
• Pseudopotential or PAW datasets, along with practical decisions around functional choice, cutoff settings, k-point meshes, convergence, and validation.
• HPC job scheduling and management using SLURM, PBS, or comparable systems.

Preferred Background

Experience in any of the following areas would be a strong advantage:

• Ferrites, spinels, perovskites, piezoelectric materials, magnetostrictive materials, or multiferroics.
• DFPT for dielectric, elastic, Born charge, or piezoelectric tensors.
• Magnetostriction, spin-orbit coupling, noncollinear magnetism, or magnetic anisotropy.
• Core/shell nanoparticle modeling or interface modeling.
• Cloud computation workflows.

Who This Role Fits

• Final-year PhD candidates in computational materials science, physics, chemistry, or related disciplines.
• Postdoctoral researchers with publications or dissertation-level work in first-principles simulation.
• MSc graduates with substantial hands-on DFT project experience.
• Industry or national-lab researchers with relevant computational materials experience.

Engagement Model

This is a paid part-time contract role, or a paid advanced internship depending on experience and availability. The work is remote and flexible. The selected candidate will receive defined calculation objectives and will be expected to deliver validated results along with short written interpretation.

Ideal Outcome

The long-term goal is to develop a computational design pipeline that can compare candidate nanoparticle materials, surface the most promising core and core/shell designs, and contribute to a broader AI-assisted ranking and prioritization framework.

Equal Opportunity and Hiring Process

Subsense is an equal-opportunity employer and values a diverse, inclusive workplace.

The company may use AI tools to support parts of recruitment, including resume review, application analysis, and signal checking for inconsistencies or verification indicators in submitted materials. These tools assist the hiring team but do not replace human judgment, and final decisions are made by people. Applicants can request more information about how their data is processed by contacting the company.