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Americas, United States, Palo Alto

Posted 218 days ago

Quantum Cheminformatics Scientist

🏢 PsiQuantum

USD 140K - 175K per year

+2 benefits
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Role Type

🧪 Scientist / Researcher

Role Focus

📈 Apply Quantum

Seniority

🌸 PhD / Postdoc
🌿 Experienced

Employer Type

🏢 Industry

As a Quantum Cheminformatics Scientist, you will use quantum computing and advanced machine learning to solve complex problems in chemistry and materials science. This role involves developing new methods for drug design, catalysis, and energy materials by combining quantum algorithms with machine learning techniques. You will focus on linking fault-tolerant quantum computing with cheminformatics and machine learning to speed up the discovery of molecules and materials, directly impacting real-world applications.

Key Responsibilities

Conduct research and develop workflows that combine quantum computing and machine learning for molecular modeling and property prediction.

Develop and apply machine learning models to accelerate the discovery of molecules and materials in drug design, catalysis, and energy materials.

Collaborate with quantum algorithm experts to identify the most impactful applications of quantum computing in cheminformatics and materials discovery.

Lead collaborative projects to integrate quantum and machine learning insights into cheminformatics workflows.

Stay updated on advancements in AI, computational chemistry, and quantum computing, serving as a subject matter expert.

Required Skills

Ph.D. in cheminformatics, computational chemistry or physics, chemical or materials engineering with a focus on machine learning for molecular modeling or materials discovery, plus 0-6 years post-PhD experience.

Hands-on experience developing and applying machine learning models in cheminformatics, drug design, or materials science.

Expertise in Python programming and familiarity with scientific libraries and machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.

Demonstrated ability to develop computational workflows integrating machine learning and molecular modeling tools.

Published peer-reviewed articles in molecular modeling, applied machine learning, or cheminformatics.

Nice-to-have Skills

Experience with molecular modeling techniques, including quantum chemistry methods, molecular dynamics, or machine learning potentials.

Familiarity with techniques for property prediction, structure-to-function modeling, or molecular fingerprint generation.

Hands-on experience with GPU-accelerated machine learning workflows or high-performance computing for cheminformatics.

Exposure to generative AI and its applications in chemistry or materials science.

Experience applying quantum computing or hybrid quantum-classical approaches to cheminformatics problems.

Technology Tags

Molecular physics

The role focuses on molecular modeling and property prediction for applications like drug design and catalysis.

Quantum ML

The role specifically focuses on applying quantum machine learning techniques to cheminformatics problems.

Hybrid algorithms

The role involves applying hybrid quantum-classical approaches to cheminformatics problems.

Materials science

The role applies quantum computing and machine learning to materials science problems, including discovery and design.

Drug discovery

A key application area for this role is drug discovery and design using quantum and machine learning techniques.

Photonic Systems

The company's quantum computer architecture is based on silicon photonics, which is the platform for the applications developed.

Fault-tolerant

The role involves bridging fault-tolerant quantum computing with cheminformatics and machine learning tools.

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