Research Engineer in the Division of Engineering ComputerEngineering - Abu Dhabi, United Arab Emirates - New York University Abu Dhabi
Description
Job Description:
Support the supervisor in developing and implementing the research agenda;Conduct high-quality and innovative research primarily focused on ML methodology development for healthcare;Reproduce results of the state-of-the-art (SOTA) methods;Design and implement experiments to compare proposed work with SOTA baselines;Contribute to the preparation of manuscripts for publication in high-impact journals and conferences;Create, maintain, and document high-quality research code for reproducibility;Maintain good practice in managing and accessing sensitive medical datasets;And collaborate with scientists within the NYU Global Network and in Abu Dhabi.
Minimum Qualifications:
Currently has or is in the process of completing a Master's degree or equivalent terminal degree from a recognized institutionMaster's degree in computer science, mathematics, computer engineering, or relevant technical fieldDemonstrable research experience involving data pre-processing and preparation for machine learning modelsDemonstrable research experience in conducting experiments for training and evaluating deep neural networksAbility to use high performance computing clusterProficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow) with several years of practiceExperience in maintaining high-quality code on GithubAbility to visualize experimental results and learning curvesEffective inter-personal and team-building skillsSelf-motivated with an ability to work independently and in a team to get the work doneExcellent communication skills (oral and written communication)Willingness to learn and confront new challenges
Preferred Qualifications:
Master's thesis conducted in the area of machine learning for healthcare and related topicsFirst-author peer-reviewed published papers (or under review)Knowledge of multi-modal learning, transfer learning, graph neural networks, transformers, or self-supervised learningExperience in dealing with large medical datasets (e.g.
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