St. Jude Children's Research Hospital
Postdoc/Computational Researcher (Quantum Computing for Chemistry and Biology)
St. Jude Children's Research Hospital, Memphis, Tennessee, us, 37544
Postdoc/Computational Researcher (Quantum Computing for Chemistry and Biology)
Join an excellent team of researchers dedicated to coming closer to the mission of St. Jude Children’s Research Hospital, that no child will die at the dawn of life. The Quantum AI for Bio (QAI4Bio) Lab led by Dr. Christoph Gorgulla in the Center of Excellence for Data-Driven Discovery in the Structural Biology Department seeks a skilled and highly motivated Postdoc or Computational Researcher in quantum machine learning. Our research group is focused on developing state‑of‑the‑art computational methods for ligand/drug discovery, using machine learning, high‑performance/cloud computing, and quantum chemistry and quantum computing. Our group also includes a wet lab dedicated to experimentally verifying the computationally predicted results in real‑world drug discovery projects.
You will join an interdisciplinary team to push the boundaries of what’s possible at the intersection of artificial intelligence and molecular modeling, building novel AI systems to advance discovery in chemistry, ligand discovery, and quantum approaches.
The successful candidate will have the opportunity to lead collaborative projects, mentor junior scientists and students, and contribute to high‑impact publications. By working together in a collaborative and intellectually stimulating environment, you will have the opportunity to make a lasting impact on the lives of children fighting cancer and other life‑threatening diseases.
The position can be a Postdoc position or a Computational Researcher position, depending on the preference of the candidate.
Key Responsibilities
Develop and optimize quantum machine learning methods for problems in molecular modeling (e.g. drug discovery and/or quantum chemistry)
Collaborate with computational chemists, structural biologists, and experimental scientists within the QAI4Bio Lab and the broader Structural Biology Department.
Collaborate closely with domain scientists to define impactful research directions and translate theory into practice.
Contribute to large‑scale computational pipelines for tasks such as molecular property prediction, drug discovery, or quantum circuit design.
Publish high‑quality research in top‑tier journals and conferences.
Work with colleagues to deploy models into production research platforms or scientific software tools.
Required Qualifications
Proven hands‑on experience (3+ years preferred) in quantum computing and quantum machine learning research and development.
Proficiency in deep learning frameworks such as PyTorch or TensorFlow.
Proficiency in quantum computing frameworks such as Qiskit, CUDA Q, Pennylane, etc.
Excellent programming skills in Python.
Ability to work independently in a fast‑paced, interdisciplinary environment.
Preferred Qualifications
PhD in Computer Science, Chemistry, Physics, Engineering, or a related discipline.
Demonstrated expertise in applying geometric deep learning to 3D data, such as experience with Graph Neural Networks for molecular graphs, deep learning on 3D point clouds or volumetric data for molecular structures, and/or understanding of molecular descriptors and featurization for deep learning.
Familiarity with molecular modeling software (e.g., RDKit, OpenBabel) and/or structural biology concepts.
Experience with cloud or HPC environments and GPU‑based training pipelines.
Record of publications in AI/ML and physical sciences journals or conferences.
Familiarity with quantum/chemistry software.
Work Location Memphis, Tennessee – the position may include in‑person or hybrid work arrangements; remote work may be considered with periodic travel to the campus.
Application You can apply here on LinkedIn via the job posting. Please submit the CV in PDF format and name it “
- CV.pdf”. Optional but preferred: include a cover letter in the same PDF and name it “
- CL + CV.pdf”.
#J-18808-Ljbffr
You will join an interdisciplinary team to push the boundaries of what’s possible at the intersection of artificial intelligence and molecular modeling, building novel AI systems to advance discovery in chemistry, ligand discovery, and quantum approaches.
The successful candidate will have the opportunity to lead collaborative projects, mentor junior scientists and students, and contribute to high‑impact publications. By working together in a collaborative and intellectually stimulating environment, you will have the opportunity to make a lasting impact on the lives of children fighting cancer and other life‑threatening diseases.
The position can be a Postdoc position or a Computational Researcher position, depending on the preference of the candidate.
Key Responsibilities
Develop and optimize quantum machine learning methods for problems in molecular modeling (e.g. drug discovery and/or quantum chemistry)
Collaborate with computational chemists, structural biologists, and experimental scientists within the QAI4Bio Lab and the broader Structural Biology Department.
Collaborate closely with domain scientists to define impactful research directions and translate theory into practice.
Contribute to large‑scale computational pipelines for tasks such as molecular property prediction, drug discovery, or quantum circuit design.
Publish high‑quality research in top‑tier journals and conferences.
Work with colleagues to deploy models into production research platforms or scientific software tools.
Required Qualifications
Proven hands‑on experience (3+ years preferred) in quantum computing and quantum machine learning research and development.
Proficiency in deep learning frameworks such as PyTorch or TensorFlow.
Proficiency in quantum computing frameworks such as Qiskit, CUDA Q, Pennylane, etc.
Excellent programming skills in Python.
Ability to work independently in a fast‑paced, interdisciplinary environment.
Preferred Qualifications
PhD in Computer Science, Chemistry, Physics, Engineering, or a related discipline.
Demonstrated expertise in applying geometric deep learning to 3D data, such as experience with Graph Neural Networks for molecular graphs, deep learning on 3D point clouds or volumetric data for molecular structures, and/or understanding of molecular descriptors and featurization for deep learning.
Familiarity with molecular modeling software (e.g., RDKit, OpenBabel) and/or structural biology concepts.
Experience with cloud or HPC environments and GPU‑based training pipelines.
Record of publications in AI/ML and physical sciences journals or conferences.
Familiarity with quantum/chemistry software.
Work Location Memphis, Tennessee – the position may include in‑person or hybrid work arrangements; remote work may be considered with periodic travel to the campus.
Application You can apply here on LinkedIn via the job posting. Please submit the CV in PDF format and name it “
- CV.pdf”. Optional but preferred: include a cover letter in the same PDF and name it “
- CL + CV.pdf”.
#J-18808-Ljbffr