The posting
Job Summary: The Singapore UCSD RNA Frontier (SURF) program aims to foster strategic collaboration between UCSD and Singapore towards the eventual development of RNA therapeutics. Within this area, SURF projects will involve the development of platforms, modalities or gene targets across multiple disease areas. Leveraging the expertise of Prof. Eugene Yeo, an RNA technologist and renowned expert in RNA biology, SURF will function in close synergy with the UCSD RNA Centre, while complementing and partnering existing national efforts in the RNA space.
We are seeking a post-doctoral chemist or biochemist to lead small-molecule discovery efforts within this program. Working closely with the senior scientists and the bioinformatician, this role will involve: 1) designing and executing small-molecule and library screening campaigns against RNA and RNA-binding protein targets, 2) applying computational and AI-driven structural modeling to guide hit identification and optimization, 3) analyzing screening data and troubleshooting unsuccessful approaches, 4) maintaining detailed experimental records, 5) training and/or mentoring students and research technicians, and 6) disseminating findings at conferences or through publications. The ideal candidate will combine hands-on screening expertise with a working knowledge of modern structure-prediction and machine-learning tools.
Duties & Responsibilities:
Research
● Implement and facilitate the SURF program strategy through rigorous, reproducible experimentation and data generation.
● Design, optimize and execute screening assays to identify small molecules that modulate RNA or RNA-binding protein function, including:
○ Biochemical and cell-based small-molecule screening assays.
○ Library-based screening approaches, such as DNA-encoded libraries (DEL), fragment libraries or other compound collections.
○ Hit validation, including binding, selectivity and structure-activity relationship studies.
● Apply computational tools, including Alpha Fold- and ESM-type structural predictions, docking and ligand-target modeling, to prioritize targets, interpret screening results and guide compound selection.
● Explore the use of AI and large language models to accelerate experimental design, data analysis and literature synthesis.
● Stay up-to-date with the latest findings and technological advances in the scientific literature and actively explore how they may be used to improve experimental design or execution strategy.
Lab Operations
● Contribute to the continuous improvement of laboratory workflows and standard operating procedures.
● Maintain detailed experimental records and compound data, and ensure adherence to best practices for reproducibility and data integrity.
● Support day-to-day laboratory operations, including reagent and compound management, equipment maintenance, and adherence to safety protocols.
Collaboration & Integration
● Work closely with the senior scientists and bioinformatician to ensure appropriate experimental design and integration of computational and experimental results.
● Collaborate with fellow researchers and external partners to advance multi-disciplinary projects.
● Participate actively in lab meetings, project discussions, and cross-functional research reviews.
Communication & Training
● Prepare and submit manuscripts to peer-reviewed journals.
● Present research findings at scientific conferences and at stakeholder meetings.
● Train or mentor junior lab members, students, or technicians.
Required Education:
- PhD in chemistry, biochemistry, chemical biology, medicinal chemistry, or a closely related discipline, with 1–5 years of postdoctoral research experience in an academic or industrial setting.
Other Desired Qualifications / Experiences / Skills:
- Hands-on experience with drug or small-molecule screening assays, and/or library screening platforms (e.g., DNA-encoded libraries, fragment or phenotypic libraries).
- Experience with structural modeling software beyond visualization tools, including Alpha Fold- and ESM-type structure prediction of targets and ligand complexes, and molecular docking.
- Familiarity with AI/machine-learning approaches or large language models, and their application to drug discovery.
- A high degree of scientific curiosity, and an ability and willingness to think creatively or apply prior knowledge from disparate experimental domains to solve novel experimental challenges or develop new assays.
- An ability to independently plan, execute and troubleshoot complex experiments.
- A track record of productivity in the form of peer-reviewed publications or submitted manuscripts.
- Experience working in interdisciplinary or collaborative research teams.
- Experience with RNA-targeting small molecules, RNA-binding proteins, or biophysical binding assays (e.g., SPR, ITC, thermal shift) is a plus.
- Exposure to translational research or early-stage drug discovery environments is a plus.
