CAREER OPPORTUNITIES AT BII

The Agency for Science, Technology and Research (A*STAR) is Singapore's lead public sector agency that fosters world-class scientific research and talent to drive economic growth and transform Singapore into a vibrant knowledge-based and innovation driven economy. In line with its mission-oriented mandate, A*STAR spearheads research and development in fields that are essential to growing Singapore's manufacturing sector and catalysing new growth industries. A*STAR supports these economic clusters by providing intellectual, human and industrial capital to its partners in industry.

A*STAR oversees 18 biomedical sciences and physical sciences and engineering research entities, located in Biopolis and Fusionopolis, as well as their vicinity. These two R&D hubs house a bustling and diverse community of local and international research scientists and engineers from A*STAR's research entities as well as a growing number of corporate laboratories.

Please proceed to the A*STAR Career Portal for the updated list of availble positions we have in Bioinformatics Institute (BII).

Positions are available until filled and only shortlisted candidates will be notified.

A postdoctoral research fellow position in the area of computational toxicology is available in the Loo lab at the Bioinformatics Institute (BII), A*STAR. The group is interested in studying chemical-induced toxicity using quantitative data modeling and cellular imaging methods. The group has recently developed the first high-throughput cellular imaging and computational platform for predicting kidney toxicity.

The successful candidate will be part of an interdisciplinary team that develops novel in vitro and computational models for predicting the toxicity of drugs, food ingredients, industrial chemicals, and environmental pollutants. He/she will develop new computational methods to analyze and model cellular responses based on chemical structures, cellular phenotypes, and gene and protein expression profiles. The candidate will have the opportunity to work in a highly stimulating environment, and participate in the global effort to develop next-generation and animal-free technologies for chemical safety assessment.

Qualifications:

Candidates must have a strong quantitative background, with a Ph.D. in either biomedical engineering, computational biology, chemical engineering, chemistry, toxicology, pharmacology, computer science, or other related fields. Candidates must also have a minimum 3 years of experience working on computational toxicology related research problems. Strong knowledge in machine learning, data mining, regression analysis, statistics, and programming (R and/or Python) are required. Candidates with previous experience in chemical risk assessment, PBPK modeling, cheminformatics, bioinformatics, or QSAR modeling are especially encouraged to apply. Candidates must also possess good communication skills, and be fluent in both spoken and written English.

Application Procedures:

Applicants should send the following documents to Dr. Loo Lit Hsin

  1. CV
  2. One-page cover letter briefly describing prior research experience and accomplishments
  3. One-page statement of research interests
  4. Contact information of two references
  5. Reprints of one to two publications

One postdoctoral position will be available from April 2019 in the Structure-based Ligand Discovery and Design group led by Dr. Hao Fan at the Bioinformatics Institute, A*STAR Singapore. Fan lab focuses on developing computational methods to study protein-ligand interactions, applying to GPCRs, transporters, and enzymes.

We are seeking one experienced research fellow (both senior and junior levels are fine) starting April 2019 who will use a wide range of computational techniques especially protein structural modelling and molecular docking to predict/design proteins/ligands and study molecular mechanisms.

Requirements:

  1. PhD in computational biology, broadly defined
  2. Strong expertise in protein structural modelling and molecular docking
  3. Experience in machine learning methods of protein-ligand interactions is preferred
  4. Experience in QM calculation of enzyme reactions or MD simulation of membrane proteins will be a plus
  5. Strong problem-solving skills and able to work independently, experience in method development is preferred
  6. Strong programming/scripting skills and English language skills
  7. Experience in teamwork (e.g. collaboration with experimental labs) will be a plus

Application Procedures:

Applicants should send a single pdf containing a one-page cover letter describing prior research/work experience and accomplishments, and a full resume, to Fan.

Only shortlisted candidates will be notified.