We are looking for a highly motivated and passionate computational scientist with a deep understanding of chemistry/cheminformatics to join our dynamic interdisciplinary team innovating at the nexus of chemistry and biotechnology for sustainable chemical manufacturing and bioproducts development. The responsibilities of the successful candidate will include but are not limited to:
· Responsibilities: Working on feature engineering, data augmentation, and hyperparameter optimization to improve the predictive accuracy of AI models
· Implement deep learning models (neural networks, convolutional neural networks, recurrent neural networks, generative AI..etc) for tasks such as molecular property prediction, molecular design, and chemical space exploration.
· Utilize natural language processing (NLP) techniques to analyze scientific literature and extract relevant information
· Collaborate with an interdisciplinary team across chemistry, biology, and data science to develop and implement AI solutions, machine learning models and algorithms
· Analyze data and present research findings at conferences, meetings, and to stakeholders while maintaining accurate records
· Write and publish high impact papers in peer-reviewed journals, alongside preparation of technical disclosures and patent applications
· Support the Division in project ideation, technology development, and grant applications
· Supervise and mentor junior researchers, fostering a collaborative and supportive work environment
· Other duties as assigned by the manager
Requirements:
-Ph.D. in chemistry, data science, machine learning, cheminformatics, computational chemistry or a related field.
-Proven experience in developing and deploying machine learning/deep learning models for chemistry or a related field.
-Strong programming skills in Python (required).
-Proficiency with machine learning libraries such as TensorFlow, PyTorch, Scikit-Learn, etc.
-Familiarity with large language models (LLMs) or computational quantum chemistry (e.g. DFT with Q-Chem) is a plus.
-Familiarity with cheminformatics libraries such as RDKit, molecular docking methods, QSAR, chemical space analysis, with a particular focus on natural product chemistry, will be highly regarded.
-Effective communication and ability to work in cross-functional teams.
-Strong time management and organisational skills.
-Adaptable mindset, open to learning new skills, and willing to assist with various other tasks and experiments.
The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.
