About this position
- Postdoctoral Position in AI-Guided Focused Antibiotic and Prodrug Design
- The Reker Laboratory in the Department of Biomedical Engineering at
- Duke University is seeking a highly motivated and creative
- Postdoctoral Associate in computational chemistry, cheminformatics,
- and/or molecular machine learning to be part of an integrated
- computational and experimental team working to create focused
- antibiotic prodrugs that treat bacterial infections while sparing the
- gut microbiome. Our team develops artificial intelligence and machine
- learning approaches to design and optimize drug molecules. In this
- project, we aim to create small-molecule prodrugs that remain inactive
- outside the site of infection and are selectively activated in
- response to infection-associated environmental triggers. The
- successful candidate will develop predictive and generative molecular
- models to identify infection-responsive linker chemistries and
- optimize prodrug structures for properties including selective
- activation, intestinal absorption, stability, solubility, and
- microbiome sparing. A major emphasis of this position is prospective,
- experimentally driven molecular machine learning. Computationally
- designed molecules will be prioritized for synthesis and biological
- testing by collaborating experimental chemists and microbiologists,
- with experimental results rapidly returned to the computational team
- to guide subsequent design cycles. The position is therefore
- particularly well suited for a highly motivated, result-oriented
- scientist who is excited to see computational predictions translated
- into synthesized molecules and experimental outcomes, and who enjoys
- working closely as part of a team spanning computational and
- experimental disciplines. Duties and Responsibilities Design and
- perform computational research related to funded programs,
- specifically: Develop and evaluate predictive machine learning models
- for small-molecule and prodrug properties, including
- infection-responsive activation, metabolism, physicochemical
- properties, and ADMET. Develop and deploy generative molecular design
- approaches for infection-responsive linkers, prodrugs, and related
- small-molecule structures. Apply cheminformatics, molecular
- representations, structure-based modeling, and multi-objective
- optimization to prioritize experimentally actionable candidates. Work
- closely with synthetic chemists and microbiologists to design
- prospective experiments and incorporate experimental results into
- iterative design–synthesize–test–learn cycles. Analyze computational
- and experimental datasets and maintain reproducible computational
- workflows, code, and research records. Communicate research activities
- through reports, manuscripts, abstracts, presentations, and
- interdisciplinary project meetings. Contribute to the overall
- functioning of the research group and collaborative project, including
- mentoring graduate and undergraduate researchers and supporting a
- collegial and inclusive team environment. Qualifications By the start
- date, applicants should have a Ph.D. in computational chemistry,
- cheminformatics, chemical engineering, biomedical engineering,
- computer science, pharmaceutical sciences, chemistry, or a related
- discipline, with demonstrated experience applying computational
- methods to molecular problems. Strong candidates will have
- demonstrated expertise in one or more of the following areas:
- Molecular machine learning and deep learning Cheminformatics and
- molecular representations Generative molecular design Computational
- chemistry or structure-based drug design ADMET/property prediction
- Multi-objective molecular optimization Drug discovery or medicinal
- chemistry informatics Proficiency in Python and experience with modern
- machine-learning frameworks and cheminformatics tools are expected.
- Experience in organic/small-molecule synthesis, medicinal chemistry,
- microbiology, bacterial assays, or antibiotic research is advantageous
- but not required. Candidates should demonstrate strong communication
- skills, a record of independent research productivity, and enthusiasm
- for working both independently and collaboratively in an
- interdisciplinary, milestone- and goal-oriented research environment.
- We particularly encourage applicants who enjoy building computational
- methods that are prospectively tested in the laboratory, learning from
- experimental successes and failures, and rapidly translating those
- results into improved molecular designs. For more about the Reker
- Laboratory, visit: https://rekerlab.pratt.duke.edu/ Appointment
- Details Start date: October 2026 (flexible) Duration: The initial
- appointment is for one year, with the possibility of renewal by mutual
- agreement and subject to continued funding from a milestone-oriented
- sponsor. To apply: Email daniel.reker@duke.edu with a CV that includes
- a link to a public GitHub repository (or similar code portfolio)
- highlighting your previous work in molecular machine learning; a cover
- letter describing your research background, computational expertise,
- research interests, and professional goals; and contact information
- for three references. You will be contacted if we are interested in
- exploring your credentials further. Applications will be accepted
- until the position is filled.
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.