Postdoctoral ML & AI for Science & Engineering

Sandia National Laboratories

New Mexico

Presencial

USD 70 000 - 110 000

Tempo integral

Há 3 dias

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Currículo e carta de apresentação personalizados, prontos a enviar.

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Vantagens oferecidas por esta oferta de emprego

Flexible work arrangements

Relocation assistance

Generous vacation

401k plan

Resumo da oferta

Sandia National Laboratories seeks multiple Postdoctoral Appointees in Machine Learning and Artificial Intelligence to advance end-to-end AI-for-science research. You will develop novel ML/AI architectures, combining physics-informed and data-driven methods to accelerate discovery and mission work across diverse applications.

Collaborate with multidisciplinary teams to deploy ML pipelines using modern frameworks and containerized workflows in distributed environments.

Qualificações

Ph.D. earned within past 5 years or expected within 6 months.
Ability to obtain and maintain DOE Q-level security clearance.
Strong analytical, written, and verbal communication skills.
Ability to work collaboratively in multi-disciplinary environments.
Experience in machine learning, deep learning, or AI research.
Experience deploying LLM-enabled software and multi-agent frameworks.
Proficiency in Python and major ML frameworks (PyTorch, TensorFlow, JAX, HF Transformers).
Working with diffusion models, LLMs, or VAEs.
Experience with Docker, Singularity, and Git-based workflows.
Exposure to HPC or cloud-based ML development (Slurm, Kubernetes, AWS/GCP).
Leadership potential through mentoring or open-source contributions.
Deep neural networks and mechanistic interpretability knowledge.
Foundational probability, statistics, and uncertainty quantification.

Responsabilidades

Collaborate with multidisciplinary teams to conduct AI research and integrate ML/AI into scientific workflows.
Prototype and deploy ML pipelines using PyTorch/TensorFlow and containerized workflows.
Advance ML architectures including generative and multi-agent models for scientific data synthesis and design.
Apply probabilistic methods for uncertainty quantification and model credibility.
Utilize linear algebra techniques for high-dimensional inference and matrix decompositions.
Integrate ML with physics-informed or hybrid modeling approaches; explore RL, autonomous experimentation, and digital twins.
Contribute to research roadmaps and publish results through reports and open-source.
Mentor interns or junior researchers and foster inclusive research.
Work onsite at least 75% of the time.