Postdoc: AI for RNA Structure & Drug Discovery

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job description

Project background

The AI in drug discovery (AIDD) project focuses on developing innovative tools and technologies to accelerate drug discovery, with a particular emphasis on unlocking new druggable spaces, such as RNA-targeting molecules, for example. With AI-driven strategies, AIDD aims to accelerate target identification to lead discovery while also advancing novel small molecules and other therapeutic modalities for next-generation drug development.

Project background

The AI in drug discovery (AIDD) project focuses on developing innovative tools and technologies to accelerate drug discovery, with a particular emphasis on unlocking new druggable spaces, such as RNA-targeting molecules, for example. With AI-driven strategies, AIDD aims to accelerate target identification to lead discovery while also advancing novel small molecules and other therapeutic modalities for next-generation drug development.

This project aims to develop novel deep learning methods for RNA tertiary structure prediction, inspired by the breakthroughs of AlphaFold in protein structure modeling. We plan to design a robust framework that incorporates RiNALMo, our state-of-the-art RNA language model [ Penić et al., 2025 ]. Additionally, we will investigate the integration of chemical reactivity measurements to enhance accuracy. Such data, closely tied to RNA’s 3D structure, offers valuable information on secondary structure elements, base-pairing, and conformational flexibility. By leveraging these inputs, our approach seeks to bridge the gap between computational and experimental methods, with significant implications for RNA drug discovery.

Job Description

We are looking for a highly motivated postdoctoral researcher to:

• Develop deep learning-based models for RNA structure prediction
• Analyze chemical reactivity experimental data
• Incorporate chemical reactivity experimental data into the structure prediction pipeline
• Run large-scale training on high-performance computing infrastructure
• Perform model finetuning and hyperparameter optimization
• Evaluate models on experimental data
  
  

Profile

We Welcome Applications From Candidates With

• A PhD in computer science, computational biology, computational chemistry, applied mathematics, physics, or a related field
• Proven experience in deep learning research and development
• Publication record at top-tier AI conferences (e.g., NeurIPS, ICLR, ICML, CVPR, ICCV, ACL, etc)
• Strong experience in Python programming and solid software engineering skills
• Experience with biomolecules and/or high-performance computing is a plus
• Interest in biology, biomolecules, or genomics (prior expertise not required)
• A structured, independent, proactive and collaborative working style
  
  

We offer

• A fully funded position with an internationally competitive salary
• Professional development opportunities, including support for grant applications and participation in conferences and workshops
• Access to state-of-the-art research infrastructure, including NSCC’s high-performance computing clusters
• A dynamic, interdisciplinary, and collaborative research environment
• The position is initially offered for one year, with the possibility of renewal for up to three years
  
  

How To Apply

We look forward to receiving your application with the following documents:

• Letter of Motivation
• CV
• Diplomas & Transcripts
  
  

We accept applications submitted via email directed to Prof. Šikić at [e-mail].