Your mission
Anchor AI-driven materials discovery in first-principles quantum chemistry
AI can learn powerful correlations. Quantum chemistry tells us which ones make sense.
At Dunia, we are building AI systems that propose experiments, guide discovery, and influence real-world decisions in materials science. As Quantum Chemistry Specialist, you are the person who ensures these systems stay grounded in first principles.
You bring deep theoretical understanding to a fast-moving, AI-driven discovery loop. Your work clarifies what must be explained, what can be approximated, and where models are extrapolating beyond physics.
This role is for someone who wants theory to matter.
Your tasks will include:
Be the physical reference point
- Apply quantum chemical methods to catalytic and materials systems under active investigation
- Provide first-principles insight into reaction mechanisms, energetics, and structure–property relationships
- Decide which phenomena demand theoretical explanation and which do not
Shape AI from the inside
- Work directly with ML researchers on representations, constraints, and inductive biases
- Help prevent physically impossible or misleading model behavior
- Influence how uncertainty and limits of validity are handled
Connect theory to experiments
- Interpret experimental results through a quantum chemical lens
- Help resolve discrepancies between models, simulations, and measurements
- Inform which experiments are most theoretically informative
Operate in a real system
- Run simulations that balance rigor with speed and scale
- Build workflows that others can rely on and extend
- Accept that usefulness matters more than elegance
Your profile
- PhD in chemistry, materials science, or a closely related field, with3–6 years of post-PhD experience applying quantum chemical methods to real chemical or materials systems
- Strong grounding infirst-principles electronic structure methodsand their practical limitations
- Experience using theory to interpret experiments,validatemodels, or guide experimental direction
- Solid programming skills and comfort building reproducible computational workflows
- Able to balance rigor with pragmatism, choosing therightlevel of theory for the question at hand
- Comfortable collaborating closely with ML researchers, experimental scientists, and automation teams
- Motivated by impact and insight, not theory in isolation
- Fluent in English,additional language desirable
