About Subsense Subsense is a deep-tech company developing the world's first non-surgical, bidirectional brain-computer interface powered by plasmonic and magnetoelectric nanoparticles. Our mission is to unlock direct communication between the human brain and AI - starting with medicalapplications such as stroke recovery and moving toward cognitive enhancement for healthy users. Headquartered in Palo Alto, Subsense brings together leading scientists and engineers to redefine the future of human-machine interaction.
The Opportunity 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 You will support first-principles simulation of magnetic and magnetoelectric nanoparticle materials, including candidate core materials beyond cobalt ferrite and selected core/shell design concepts. The goal is to 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.
What You'll BringRequired skills Candidates should already have hands-on experience with:
- 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, such as pymatgen, ASE, or similar tools.
- Pseudopotential / PAW datasets and practical choices around functional, cutoff, k-point mesh, convergence, and validation.
- HPC job management using SLURM, PBS, or similar systems.
Helpful but not required Experience with any of the following would be especially useful:
- Ferrites, spinels, perovskites, piezoelectric materials, magnetostrictive materials, or multiferroics.
- DFPT calculations 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 compute workflows.
Who this role suits This role is appropriate for:
- Final-year PhD students in computational materials science, physics, chemistry, or related fields.
- Postdocs with publications or dissertation work in first-principles simulation.
- MSc graduates with strong hands-on DFT project experience.
- Industry or national lab researchers with relevant computational materials experience.
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.
Subsense is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
About Subsense Subsense is a deep-tech company developing the world's first non-surgical, bidirectional brain-computer interface powered by plasmonic and magnetoelectric nanoparticles. Our mission is to unlock direct communication between the human brain and AI - starting with medicalapplications such as stroke recovery and moving toward cognitive enhancement for healthy users. Headquartered in Palo Alto, Subsense brings together leading scientists and engineers to redefine the future of human-machine interaction.
The Opportunity 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 You will support first-principles simulation of magnetic and magnetoelectric nanoparticle materials, including candidate core materials beyond cobalt ferrite and selected core/shell design concepts. The goal is to 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.
What You'll BringRequired skills Candidates should already have hands-on experience with:
- 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, such as pymatgen, ASE, or similar tools.
- Pseudopotential / PAW datasets and practical choices around functional, cutoff, k-point mesh, convergence, and validation.
- HPC job management using SLURM, PBS, or similar systems.
Helpful but not required Experience with any of the following would be especially useful:
- Ferrites, spinels, perovskites, piezoelectric materials, magnetostrictive materials, or multiferroics.
- DFPT calculations 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 compute workflows.
Who this role suits This role is appropriate for:
- Final-year PhD students in computational materials science, physics, chemistry, or related fields.
- Postdocs with publications or dissertation work in first-principles simulation.
- MSc graduates with strong hands-on DFT project experience.
- Industry or national lab researchers with relevant computational materials experience.
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.
Subsense is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
