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AI Researcher in Physics Foundation Models

Mirror PhysicsNew York City
On-site Full-time

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Experience Level

Mid to Senior

Qualifications

QualificationsPh. D. or M. S./B. S. with a demonstrable research background in Physics, Materials Science, Computer Science, or a related field, with a strong focus on machine learning and atomistic modeling. Minimum of 3 years of experience in deep learning at scale, particularly with equivariant Graph Neural Networks (GNNs), diffusion models, or related methodologies. Proficiency in high-performance computing and the ability to work with large datasets. Strong publication record in relevant AI or physics journals and conferences.

About the job

About Us

Mirror Physics is an innovative AI company based in New York City, pioneering the next generation of scientific simulation technologies. Our mission is to create intelligent systems that grasp the fundamental principles of physics, thereby providing essential acceleration for advanced research and development across various technological fields. We are currently developing a leading-edge AI platform that predicts experimental outcomes in chemistry and materials science, seamlessly integrating physical simulation with high-throughput experimental verification. This endeavor aims to hasten discoveries in biotechnology, energy, manufacturing, and more. Supported by top-tier investors and scientific experts, we are seeking to expand our research team during this crucial period in the industry.

The Role

As the principal AI researcher focusing on physics model development, you will lead efforts in designing innovative architectures, training algorithms, and evaluation processes to transform vast amounts of physical simulation data into scalable, precise, and versatile predictive engines applicable in both scientific and industrial contexts.

Key Responsibilities

  • Create robust, scalable, and universally applicable atomistic models with high fidelity across various chemical domains.

  • Compile diverse and multi-fidelity datasets into cohesive training corpora; innovate new objectives to enhance data efficiency.

  • Produce groundbreaking datasets that encompass an unmatched variety of chemical systems, consistently computed at the highest theoretical levels suitable for general chemistries.

  • Design diagnostic tools for model performance evaluation, failure mode assessment, and uncertainty quantification; propose new benchmarks to rigorously test predictive accuracy, physical consistency, and extrapolation capabilities.

  • Develop downstream tools to improve model precision and processing speed, including model distillation and fine-tuning techniques.

  • Collaborate with the AI-for-science community through research publications and contributions at leading conferences such as NeurIPS, ICML, and ICLR.

  • Mentor junior researchers and work closely with applied science and engineering teams.

About Mirror Physics

Mirror Physics is at the forefront of AI innovation, focusing on scientific simulations that empower breakthroughs across biotechnology, energy, and manufacturing sectors. We are dedicated to fostering a collaborative and intellectually stimulating environment where creativity thrives.

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