"Path 1: Research Assistant: Laboratory for Computer-Human Intelligence"

The Laboratory for Computer-Human Intelligence in the Division of Engineering, New York University Abu Dhabi, seeks to recruit a motivated Research Assistant to work on cutting-edge machine learning / artificial intelligence / data science applications that utilize human signals (e.g. text, audio, physiology).

The Role

The Research Assistant will work on research projects in the Laboratory for Computer-Human Intelligence under the direction of the Principal Investigator Tuka Alhanai. Research projects range from implementing machine learning algorithms that measure cognitive outcomes, to visualizing patterns that map population-level health and well-being. The Research Assistant is expected to develop creative solutions to day-to-day technical challenges, think critically about meaningful next steps in the research process, and lucidly relay their thoughts, insights, and conclusions to high-level executive audiences and technical peers. It is not expected that the applicant be perfect at these tasks, but will motivated and self-driven to develop the necessary skills to master the role of a researcher.

Key Responsibilities:

• Data Curation: Develop data ingestion engine(s) (appropriate for the data type) to organize the data that simplifies processing conducted with the data. Researcher will utilize any of sensors, front-end web-based applications, and databases to collect and organize data.
• Multimodal Signal Processing: involves processing data of different types (e.g. audio, text, physiology, image, movement), using computational methods. Most data is noisy and needs to be cleaned and reduced into a salient representation.
• Machine Learning: involves applying algorithms to automatically detect patterns in data and model outcomes of interest. Many if the latest modeling approaches are little understood, and require computational power to digest information beyond what humans are able to process.
• Visualization: Representation of data and results in the form of plots, charts, and images. This helps convey to technical peers as well as the general public what contributions the work makes to knowledge. It is expected that this be rendered statically and/or dynamically in a web framework.
• Documentation: involves structuring above work in a (a) software repository, (b) comprehensive comments in software, (c) development of a clear README, (d) journal/conference paper*, and (e) corresponding project website. This ensures work is reproducible and accessible to both technical peers and the general public.
  *researcher should target top-100 publications here: http://www.guide2research.com/topconf/

Technical Experience

The successful applicant will have the following technical experience in:

• The development of end-to-end applications (e.g. databases, algorithms, visualizations).
• Processing one or more data types/signals (e.g. text, audio, images, physiology).
• Applying machine learning algorithms (e.g. regression, neural networks).
• Utilizing the following programming languages/frameworks: bash, SQL, Python, HTML, Javascript, and Tensorflow/Pytorch, as well as source code version control (git).
• Authoring peer-reviewed scientific papers.

Attitude

The successful applicant should be self-driven and interested in the general domain of machine learning (e.g. latest ideas, research findings, research tools), contain a scientific curiosity for formalizing the underlying mechanisms of the observable world (e.g. do we make conclusions based on the words in speech, or intonation of speech?), and is passionate about creating timeless systems, with an attention to detail and zeal for quality.

Further Information: For a sense of work performed in the lab, explore this portfolio: https://talhanai.xyz/projects.html

Educational Experience: The successful application will have a Bachelor’s (ideally Master’s) degree in Computer Science (or equivalent major; Mathematics, Physics, Engineering, Economics, etc.).

Applications will be considered on a rolling basis until the position is filled. An applicant that passes the review process will be accepted immediately. To be considered, all applicants must submit in PDF format:

• Cover letter.
• Curriculum Vitae.
• Transcript of degree.
• Two letters of recommendation.

This position is under the NYUAD Kawader program, for details regarding the program, open dates, specific program requirements, and FAQ's please refer to our NYUAD Kawader webpage: https://nyuad.nyu.edu/en/about/careers/postdoctoral-and-research/kawader-research-assistantship-program.html

For further information or questions regarding the position/program please contact nyuad.kawader@nyu.edu (due to the high volume of emails received, please allow 5 working days for a response)