Research Assistant in the Division of Engineering - Dr. Farah Shamout

The Clinical Artificial Intelligence Lab at the company seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We utilize large, real-world complex datasets, including data extracted from electronic health records and medical images, for applications pertaining to patient diagnostics and prognostics. We are looking for a Research Assistant to join our team and contribute significantly to the field. The researcher is expected to possess strong machine learning skills to enhance model performance and robustness, and show exemplary passion and motivation to pursue multidisciplinary research at the intersection of computing and healthcare. Methodologies of interest include multi-modal learning, foundation models including large language models, agentic AI, multi-agent AI systems, transfer learning, self-supervised learning, and federated learning. The Research Assistant will primarily be based at the company and will report directly to Dr. Farah Shamout, while collaborating closely with other researchers, PhD students, and undergraduate research assistants. The researcher will also engage with our regular collaborators across the company's campuses and local medical institutions in the UAE.

Key Responsibilities:

• Support the supervisor in developing and implementing the research agenda.
• Conduct high-quality and innovative research primarily focused on ML for healthcare.
• Design and implement experiments to compare proposed work with state-of-the-art baselines.
• Publish research findings in high-impact journals and conferences.
• Communicate and present research findings at international academic gatherings.
• Create, maintain, and document high-quality research code for reproducibility.
• Maintain good practice in managing and accessing sensitive medical datasets.
• Collaborate with scientists within the company Global Network and in Abu Dhabi.

Minimum Qualifications:

• Currently holds or is in the process of completing a bachelor’s or master’s degree from a recognized institution in computer science, mathematics, computer engineering, or a relevant technical field.
• Demonstrable research experience involving data pre-processing and preparation for machine learning models.
• Experience in conducting experiments for training and evaluating deep neural networks.
• Knowledge of multi-modal learning, transfer learning, transformers, or self-supervised learning.
• Experience working with large medical datasets (e.g., electronic health records data or medical images).
• Ability to use high-performance computing clusters.
• Proficiency in Python and relevant libraries (e.g., Pytorch, TensorFlow).
• Experience maintaining high-quality code on GitHub.
• Experience in running and managing experiments using GPUs.
• Strong inter-personal and team-building skills.
• Self-motivated with the ability to work independently and collaboratively.
• Excellent communication skills (oral and written).

Preferred Qualifications:

• Bachelor’s/Master’s thesis conducted in the area of machine learning for healthcare and related topics.
• First-author peer-reviewed published papers (or under review).
• Evidence of leadership and service activities in the academic domain.

Application Process: For consideration, applicants need to submit a cover letter, CV with a full publication list, a research statement (1-page), a project proposal summary (1-page), and three letters of reference, along with a transcript, all in PDF format.

Compensation: The terms of employment are competitive and include housing and educational subsidies for children. Applications will be accepted immediately, and candidates will be considered until the position is filled.

Contact Information: For questions, please email Prof. Farah Shamout at [email protected].