Research Technician

A global higher education leader in innovative teaching, research and public service, the University of North Carolina at Chapel Hill consistently ranks as one of the nation's top public universities. Known for its beautiful campus, world-class medical care, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn.

One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and professional opportunities that span the campus and community.

University employees can choose from a wide range of professional training opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates for performing arts events.

Our mission is to improve the health and well-being of North Carolinians and others whom we serve. We accomplish this by providing leadership and excellence in the interrelated areas of patient care, education and research.

Patient care: We promote health and provide superb clinical care while maintaining our strong tradition of reaching underserved populations and reducing health disparities across North Carolina and beyond.

Education: We prepare tomorrow`s healthcare professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly skilled workforce.

Research: We develop and support a rich array of outstanding health sciences research programs, centers and resources. We provide infrastructure and opportunities for collaboration among disciplines throughout and beyond our university to support outstanding research. We foster programs in the areas of basic, translational, mechanistic and population research.

We are seeking a highly motivated machine learning/AI scientist to join our computational drug discovery team for a short-term project (6 months, with potential extension). The successful candidate will leverage machine learning and artificial intelligence techniques to design novel molecular binders targeting specific protein structures, optimizing for binding affinity and physicochemical properties. The role involves developing and applying computational models to generate, evaluate, and refine small molecule candidates, working closely with cheminformatics and structural biology data. This position offers an opportunity to contribute to cutting-edge drug design in a dynamic, interdisciplinary environment.

Key responsibilities include:

Developing ML/AI models to predict and generate novel binders based on protein 3D structures and physicochemical descriptors.

Processing and analyzing protein-ligand interaction data to inform binder design.

Implementing generative chemistry approaches (e.g., SMILES-based models) to propose molecules with desired scaffolds and functional groups.

Validating model outputs using cheminformatics tools and physicochemical property filters.
Collaborating with team members to integrate computational predictions with experimental validation plans.

Demonstrated possession of the competencies necessary to perform the work.

Proficiency in Python programming, including libraries such as TensorFlow, PyTorch, scikit-learn, or similar for ML/AI model development.

Experience with cheminformatics tools (e.g., RDKit, ChemPy) for molecular manipulation and property calculation.

Familiarity with generative models (e.g., VAEs, GANs, transformers) for molecular design, particularly SMILES-based representations.

Understanding of protein-ligand interactions and molecular docking principles.

Course work and significant relevant experience in deep learning models for de novo molecular design (e.g., ChemVAE, MolGPT, or similar). Knowledge of scaffold-based molecular generation and functional group variation strategies. Background in handling chemical databases (e.g., ChEMBL, PubChem) and integrating diverse datasets. Exposure to experimental validation workflows in drug discovery (e.g., collaborating with wet-lab teams).

If you experience any problems accessing the system or have questions about the application process, please contact the Office of Human Resources at (919) 843-2300 or send an email to employment@unc.edu

Please note: The Office of Human Resources will not be able to provide specific updates regarding position or application status.

The University is an equal opportunity employer and welcomes all to apply without regard to age, color, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex, or sexual orientation. We encourage all qualified applicants to apply, including protected veterans and individuals with disabilities.