Postdoctoral Research Associate in Gorgulla Lab

Location

Memphis, TN

Category

Postdoc

Department

Structural Biology

Shift

Weekday Day

Position Type

Full Time

Scheduled Weekly Hours

40

JR5861

A postdoctoral research associate position is available with Dr. Christoph Gorgulla.

This project aims to advance computational drug discovery by developing and applying innovative deep learning-based methods to identify novel small‑molecule therapeutics. Tackling this challenge is critical for enabling more precise and efficient discovery efforts against complex biological targets that currently lack effective treatments, such as Parkinsons Disease. By combining machine learning with quantum chemistry and structure based approaches, the project will accelerate the translation of computational predictions into experimentally validated drug leads.

Position Responsibilities:

• Design and execute computational screening campaigns to identify and prioritize candidate small molecules for experimental validation, with a focus on developing generalizable workflows and tools.

• Develop or refinement methodologies that improve hit identification, pose prediction, or scoring accuracy, and apply them in drug discovery projects.

• Collaborate closely with other scientists within the lab and with external academic or industrial partners to translate computational predictions into testable hypotheses.

• Mentor graduate students or interns in computational techniques, reproducible workflows, and best practices in data‑driven discovery.

• Contribute to manuscript preparation, grant proposals, and presentations, with opportunities to co‑lead sections related to methods or results.

• Participate in professional development activities, including conference attendance, skill‑building workshops, or cross‑disciplinary training.

Minimum Education and/or Training:

A Ph.D. in computational chemistry, structural biology, bioinformatics, computer science, pharmaceutical sciences, or a closely related field. Candidates with significant experience in molecular modeling, virtual screening, quantum chemistry, or machine learning for drug discovery are especially encouraged to apply.

Special Skills, Knowledge, and Abilities:

• Very strong background and skills in deep learning and machine learning

• Machine learning frameworks, in particular Py Torch

• Experience in drug discovery or ability to adapt quickly to applied domains such as drug discovery

• Proficiency in programming or scripting (e.g., Python, Bash, or similar) for workflow automation and data analysis.

• Ability to think critically about experimental data and translate findings into actionable research directions.

• Excellent communication skills, both written and verbal, with the ability to present findings clearly to collaborators from diverse scientific backgrounds.

• Self‑motivated, organized, able to contribute to a team‑oriented environment.

• Computational molecular modeling, computational biology, computational drug discovery, cheminformatics, and/or quantum chemistry

• Cuda, Tensor Flow

• Familiarity with common tools for structure‑based molecular modeling or virtual

• Experience developing or benchmarking computational methods for drug discovery, scoring functions, predictive models or quantum chemistry

• Machine learning or AI frameworks applied to molecular discovery.

• Familiarity with cloud or high‑performance computing environments.

• Experience collaborating with experimental scientists or participating in multidisciplinary projects.

• Prior mentorship of students or involvement in team‑based problem solving.

• Interest in contributing to manuscripts, grant proposals, or open‑source software.

About the lab and St. Jude:

Our lab operates with a mentorship philosophy centered on trust, curiosity, and growth: trainees receive as much guidance and support as needed, while also being encouraged to explore ideas independently and build ownership over their work. We strive for a kind, collaborative, and intellectually generous team culture—one where people challenge bold scientific problems together while celebrating each other’s success. Above all, we are united by a shared drive to tackle high‑impact challenges in…