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“Please join us to tackle an extraordinary set of scientific and engineering challenges. Let’s make history together.”
—  Oren Etzioni

Allen AI Young Investigators

Duration: 1-3 years

Start date: Flexible (rolling application with no deadline)

Candidates: Are within one year of completing their PhD, or already have a PhD

Allen AI Young Investigators is a postdoctoral program offering unique benefits. The program will enable you to balance working collaboratively on an AI2 project while pursuing an independent research agenda.

Benefits

  • Dedicated AI2 mentor: Mentorship in research, grant writing, and more
  • 50% collaborative work on an AI2 project
  • 50% work on your own projects
  • Generous travel budget
  • AI2 provides support for obtaining a visa through its immigration attorney, and pays the necessary expenses
  • Access to AI2’s data, AWS infrastructure, and other resources as needed
  • No grant writing, teaching, or administrative responsibilities
  • $100K research funding from AI2 after completion (based on proposal)

See our current Young Investigators on our Team Page!

Highlighted AI2 Alumni

  • Mohit Iyyer completed his PhD at the University of Maryland, College Park. He is currently an assistant professor in computer science at UMass Amherst His research interests lie broadly in natural language processing and machine learning. His publications with AI2 include Deep Contextualized Word Representations (NAACL 2018) and QuAC: Question Answering in Context (EMNLP 2018).
  • Roy Schwartz completed his PhD at the School of Computer Science and Engineering at the Hebrew University of Jerusalem in 2016. He is currently a postdoctoral researcher at the University of Washington and a Young Investigator at AI2. Roy will continue working with AI2 until 2020, at which time he will join the faculty of the School of Computer Science and Engineering at the Hebrew University of Jerusalem. His research interests are semantic representation and syntactic parsing. His publications with AI2 include SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (EMNLP 2018), Rational Recurrences (EMNLP 2018), and LSTMs Exploit Linguistic Attributes of Data (ACL RepL4NLP Workshop 2018).