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Job DescriptionAbout This Role — The Mission
This is not a generalist AI research role.
We are looking for a researcher who has spent serious time thinking about how physics constraints interact with neural network training — and who wants to see that methodology deployed against real product development problems, not just validated on benchmark datasets. You will be the person who designs what the ML engineers build. Your acquisition functions will drive real laboratory experiments. Your PINNs methodology will run in product development. The work you originate here will be tested against physical ground truth in ways that most academic scientific ML researchers never get access to.
- Area A — Scientific ML & PINNs Methodology Origination: Originate and advance PINNs methodology — design physics-constrained loss function architectures, validate digital twin ML components against domain physics (with storage domain expert), and deliver validated prototypes with complete technical documentation to implement. As the sole PINNs methodology originator on the team — this capability cannot be delegated or substituted.
- Area B — Uncertainty Quantification & Bayesian Experimental Design (hold one of B or C): Lead research into Bayesian deep learning, active learning acquisition function design, ensemble uncertainty methods, and Bayesian experimental design frameworks for autonomous experiment selection. Transfer validated acquisition function designs for active learning pipeline integration.
- Area C — Causal ML & Reliability Modeling (substitute for B if reliability-focused): Own causal inference framework product development for reliability root cause analysis — structural causal model (SCM) design, causal discovery, and causal intervention planning for product development improvement.
- Synthetic Data Methodology : Design physics-constrained generative model approaches (diffusion models, VAEs) for synthetic data generation. Deliver validated methodology and training recipes for pipeline operationalization.
- IP & Domain InterMface: Demonstrate strong research output through preprints, or patent disclosures. Interface with storage domain expert to validate physics constraints before deployment. Produce validated research prototypes with complete technical documentation to team-handoff standard. Participate in design reviews as the research methodology authority.
Requirements
Education:
- Master's or PhD in Artificial Intelligence, Machine Learning, Physics, Applied Mathematics, or related field. Strong AI/ML research focus and scientific computing background required.
Experience:
- For Master's degree: 1–3 years work or research experience in scientific ML or applied AI roles. For PhD: Open — no minimum work experience required. Research depth is the primary criterion. Peer-reviewed publication (NeurIPS, ICML, ICLR, AAAI, Nature MI, or domain-specific venues), strong PhD research, or significant open-source scientific ML contribution.
Must have skills:
- PyTorch or JAX: Expert — deep research-level implementation capability
- Area A — Scientific ML & PINNs (Required): PINNs methodology design, physics-constrained loss function architecture, digital twin modeling. The most critical capability on the team.
- Area B — UQ & Bayesian Methods (one of B or C required): Bayesian deep learning, Bayesian experimental design, active learning acquisition function design, ensemble uncertainty quantification
- Area C — Causal ML (substitute for B if reliability-focused): Structural causal models (SCM), causal discovery, causal inference for reliability and yield root cause analysis
- Demonstrated Research Output: Publication, preprint, PhD thesis chapter, or significant open-source scientific ML contribution in at least one primary area
- Prototype-to-Documentation Handoff: Produce validated research prototypes with complete technical documentation
Good to have skills:
- Diffusion models (DDPM, conditional diffusion) — physics-constrained synthetic data generation
- Graph neural networks (GNN) — materials property prediction, failure propagation modeling
- Neural ODEs — dynamic systems and degradation trajectory modeling
- Foundation model fine-tuning — domain adaptation for scientific tasks
- RL for scientific discovery — exploration strategies in experimental search spaces
- Top-venue publication (NeurIPS / ICML / ICLR / Nature MI) — strong bonus signal
- Materials science, semiconductor, or precision product development domain background
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