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✓ 100% verification score · Source: Midjourney (ashby) · Always confirm final requirements on the original source.
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Overview
WHAT YOU’LL DO
Full job description
WHAT YOU’LL DO
- Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy.
- Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements.
- Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks.
- Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation.
- Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes. WHAT WE’RE LOOKING FOR
- Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains.
- Ability to move fluidly between research prototypes and production-quality systems.
- Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility.
- A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.) USEFUL EXPERIENCE
- ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints.
- Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts.
- A background in computational physics or scientific computing.
- Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging.
- Experience in Agentic-SciML is a plus.
- Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines.
- Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).
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