Are you a weirdo, passionate about science, a bit of a rockstar and above average geeky, driven by the idea of leaving a positive imprint of the world? Then come join us!
Below are our current openings. You are also welcome to send a general hello to
Are you a driven sales person with experience from the academic space, and a desire to equip researchers and students with the best tools out there?
We are looking for a digital marketeer / growth hacker to take the individual subscription model for the Iris.ai literature review tools from the current small pilot and scale it globally.
We are looking for an intern to be part of the Iris.ai team, with the goal of full term employment at the end of the internship.
RQ: What is the best way of introducing specialized, domain specific knowledge into an existing general word embedding model, in order to produce high quality embeddings for domain specific concepts?
RQ: What is the best method to classify a concept occurring in a text into a fixed set of classes (such as e.g. “chemical element”, “chemical property”, “process”, …) given its position in the text and its context?
RQ: What is the best way to label a cluster of documents with a sequence of words? (By “a sequence of words”, we do not specifically refer to “sentences”. It can be a list of words highly-ranked statistically.)
RQ: What is the best way to determine the semantic relationship between two texts? For example, given two texts discussing similar topics in their contents, how do we identify the dependencies between the contexts, e.g. one text is cited in the other, etc., based purely on their semantic properties?