Quantum Cheminformatics Professional

2 days ago

Brisbane, Queensland, Australia beBeeQuantumCheminformatics Full time $120,000 - $180,000

We are seeking a highly skilled professional to develop and apply machine learning models to accelerate molecular and materials discovery in areas such as drug design, catalysis, and energy materials.

Key Responsibilities:
  • Conduct innovative research and develop workflows that combine quantum computing and ML-driven cheminformatics for molecular modeling and property prediction.
  • Develop and apply machine learning models to accelerate molecular and materials discovery in areas such as drug design, catalysis, and energy materials.
  • Collaborate with quantum algorithm experts to identify areas where quantum computing can have the greatest impact in cheminformatics and materials discovery.
  • Act as a technical lead in collaborative projects, working with internal and external teams to integrate quantum and ML-driven insights into cheminformatics workflows.
  • Serve as a subject matter expert in ML-driven cheminformatics, staying updated on recent advancements in AI, computational chemistry, and quantum computing.
  • Document and communicate research findings through internal reports, peer-reviewed publications, and conference presentations.
Requirements:
  • Ph.D. in cheminformatics, computational chemistry or physics, chemical or materials engineering, or closely related fields, with a strong focus on ML methodologies for molecular modeling or materials discovery.
  • Hands-on experience developing and applying machine learning models to solve real-world problems in cheminformatics, drug design, or materials science.
  • Expertise in Python programming and familiarity with scientific libraries and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
  • Demonstrated ability to develop computational workflows integrating ML and molecular modeling tools.
  • Published peer-reviewed articles in the field of molecular modeling, applied machine learning, or cheminformatics.
Preferred Qualifications:
  • Experience with molecular modeling techniques, including quantum chemistry methods (e.g., DFT, coupled cluster theory, or wavefunction-based 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 ML workflows or high-performance computing for cheminformatics applications.
  • Exposure to generative AI (e.g., large language models) and its applications in chemistry or materials science.
  • Experience applying quantum computing or hybrid quantum-classical approaches to cheminformatics problems.

PsiQuantum provides equal employment opportunity for all applicants and employees. We do not unlawfully discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, or any other basis protected by applicable laws.

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