(Senior) Scientist, Healthcare and MedTech Division, I2R

Roles & Responsibilities

Job Description

A research scientist position is available immediately at the Institute for Infocomm Research (I2R), A*STAR, Singapore. The position will focus on advancing AI approaches based on real-world health record datasets for digital health and precision medicine applications.

Specific areas of focus include:

• Machine learning and deep learning with large real-world health datasets (electronic health records, clinical text, lifestyle and genomic data)
• Integration of biomedical knowledge representations and machine learning/deep learning approaches
• Learning with big observational, irregularly sampled or noisy datasets
• Uncertainty quantification and interpretable machine learning/deep learning
• Applications in precision screening, stratification, phenotyping, health outcome prediction, biomarker identification and health promotion

The role will interface unique real-world health datasets with cutting edge AI technologies and clinical translation targets. Successful candidates should have experience and intuition across real-world data understanding and engineering, machine learning problem formulation, AI model and system development, and translation of technology solutions into real-world implementation.

Core responsibilities include R&D project execution, translation of novel AI technology solutions to clinical evaluation and healthcare implementation, as well as drafting of proposals and project scoping. The position entails working in a highly inter-disciplinary R&D team in close collaboration with experts in machine learning, public health, bioinformatics, precision medicine as well as with clinicians, health ecosystem stakeholders, and government entities on ambitious projects that have the potential to transform patient care and deliver improved health outcomes.

Job Description

• Ph.D. in Biomedical/Health Informatics, Biomedical Engineering, Computer Science, Statistics, Electrical Engineering, Applied Mathematics or other Data Science intensive fields.
• Candidates should have a strong computational background, experience with large real-world datasets and demonstrated research expertise in at least two or more of the following areas:

· Deep learning / machine learning for healthcare applications (including data annotation, problem formulation and state-of-the-art techniques)

· Development of AI solutions for real-world digital health applications

· Large-scale biomedical or healthcare data engineering and data science (incl. warehousing, databases, ETL pipelines, exploratory analyses, knowledge extraction/representations and/or data mining)

· Natural language processing / text mining

· Biomedical/health informatics

· Quantitative or qualitative evaluation studies

• A strong track record with publications in leading digital health, data science, applied machine learning/mathematics/ statistics venues is required. Experience working in large interdisciplinary project teams with a health or medical technology translation perspective is a plus.
• 8 years post-PhD with track record in applied health informatics projects and/or publishing research in leading digital health venues. Experience in healthcare, corporate or application-oriented environments desirable.
• Keen experience and intuition for working with large, complex real-world biomedical and health datasets
• Ability to innovate with advanced data science and AI methods as well as to direct prototyping for demonstrating research ideas
• Understanding of clinical environment and workflows
• Exposure to varied digital health study designs, research methods, human computer interaction frameworks, and clinical evaluation approaches
• Cross-disciplinary experience spanning clinical needs definition, R&D problem formulation, health or medical technology evaluations, impact evaluation and/or real-world implementation.
• Ability to work independently as well as in multidisciplinary teams with strong interpersonal skills
• Good communication skills for publications, reports and proposals.
• Quick learner; able to acquire the necessary domain knowledge
• Agility in dynamic project environments with impact-oriented mindset

Drive to keep pace with AI/ML R&D developments, work with full stack data engineering and deployment teams for real world projects, and with healthcare ecosystem partners on clinical translational studies highly desirable.

• Strong programming abilities (e.g. Python, Bash, PySpark, R, C/C++, Java, Perl)
• Strong quantitative skills including deep knowledge of statistics, probability, modern machine learning and deep learning
• Experience with ETL processes, data preprocessing, data science, and data visualization tools, especially for large or multimodal health datasets (e.g., SAS, Tableau, PowerBI, Knime, WEKA, Jupyter notebooks etc)
• Experience with biomedical knowledge representation and model development frameworks
• Comfort with ML/DL frameworks (e.g., PyTorch, TensorFlow), data science and data mining tools (Jupyter notebooks, SAS, Tableau, Knime, WEKA, visualization libraries etc) as well as NLP/LLM toolkits
• Comfort within cloud-based data engineering and ML environments
• Exposure to MLOps/DevOps infrastructure and pipelines for deploying AI/ML solutions for healthcare applications

Motivated applicants with significant domain expertise and strong programming skills who are looking to switch into AI related fields and committed to building robust and scalable approaches for population-scale healthcare impact will also be considered.

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..

Tell employers what skills you have

Health Promotion
PySpark
Human Computer Interaction
Public Health
Health Informatics
Biomedical Engineering
Data Engineering
PowerBI
PyTorch
Bioinformatics
Quantification
Weka
Prediction
Text Mining
Applied Mathematics