Postdoc; Deep Learning Research​/Engineering

Postdoc (Deep Learning Research/Engineering)

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 (QAI4

Bio) 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 deep learning researcher/engineer
. Our research group focuses 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. The group also includes a wet‑lab dedicated to experimentally verifying computationally predicted results in real‑world drug discovery projects.

We are seeking a highly motivated postdoc with deep expertise in machine learning and deep learning. 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 or a Computational Researcher, depending on the preference of the candidate.

• Research, develop, and optimize deep learning architectures (e.g., for drug discovery and/or quantum chemistry)
• Work extensively with complex 3D and multimodal data
• Deploy and operationalize models
• Tune the performance of deep learning models
• Stay at the frontier of deep learning
• Collaborate with domain experts, e.g., computational chemists, structural biologists, and experimental scientists within the QAI4
  
  Bio Lab and the broader Structural Biology Department
• Publish high‑quality research in top‑tier journals and conferences
• Work with colleagues to deploy models into production research platforms or scientific software tools

• Proven hands‑on experience (3+ years preferred) in deep learning research and development
• Proficiency in deep learning frameworks such as PyTorch or Tensor Flow
• Deep understanding of deep learning
• Experience with many types of deep learning models (e.g., GNNs, CNNs, transformers, diffusion models, …)
• Excellent programming skills in Python
• Ability to work independently in a fast‑paced, interdisciplinary environment

• PhD in Computer Science, Mathematics, Chemistry, Physics, Engineering, or a related discipline
• Familiarity with molecular modeling software (e.g., RDKit, Open Babel) and/or structural biology concepts is highly desirable
• 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

St. Jude Children’s Research Hospital is a world‑class research institution dedicated to pediatric cancer and other catastrophic diseases of childhood. It is the first and only National Cancer Institute (NCI)‑designated Comprehensive Cancer Center devoted solely to children. Our 300 faculty work across the spectrum of basic, translational, clinical, and population science in a highly collaborative multidisciplinary environment that includes a Nobel laureate and members of the National Academy of Science and the National Academy of Medicine.

St. Jude is located in the heart of Memphis, Tennessee, a vibrant and friendly city at the historic American crossroads of music, trade, food, and culture. Living in Memphis provides several unique advantages, including breathtaking nature, rich culture, and affordability.

We highly value the dynamic and collaborative environment fostered by in‑person or hybrid work arrangements, which allows for seamless direct engagement and deep team synergy. In exceptional cases, we are also open to exploring fully remote work, with regular periodic travel to our Memphis campus for essential meetings and key collaborative sessions.

You can apply on Linked In via the job posting. Please submit the CV in PDF format and name it " - CV.pdf". Optional (but preferred): add a cover letter to the CV in one combined PDF and name the document " - CL + CV.pdf".