Scientist, AI in Drug Discovery (GIS) - A •STAR RESEARCH ENTITIES

Roles & Responsibilities

The Genome Institute of Singapore (GIS) is the national flagship for genomic sciences, driving cutting-edge research at the intersection of biology, engineering, and medicine. This position is offered in the Laboratory of AI in Genomics, led by Prof. Mile Sikic, which uses advanced bioinformatics and deep learning approaches to develop next-generation models for genomic data analysis. We are group a of computer scientist with a mission to improve healthcare using advance deep learning models. Located in the heart of Singapore's thriving biomedical hub, GIS offers a dynamic and collaborative environment, with close ties to world-class universities (NUS and NTU), pharmaceutical companies, and biotech start-ups. Joining our team means working on transformative projects with real-world impact, while benefiting from Singapore's vibrant research ecosystem and strong support for innovation.

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 [ Penic 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 :

\We offer

How to apply

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

We accept applications submitted through our online application portal or via email directed to Prof. Sikic at mile_sikic@a-star.edu.sg.

The above eligibility criteria are not exhaustive. A

Tell employers what skills you have

Genomics

Drug Development

Target Identification

Medicine

GIS

3D

Therapeutic Modalities

Research and Development

Biology

Drug Discovery

Bioinformatics

RNA

Computational Biology

Prediction

Python Programming

Applied Mathematics