Join us
Hello!
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
talent@iris.ai.
Sales manager
Rolling applications
Sales manager for AI tools to University Libraries and research institutes
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?
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?
Learn more
Digital Growth management
Rolling applications
Scaling digital tools to the world’s most scrutinizing audience: researchers
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 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.
Learn more
Business Internship
Rolling applications
Internship: admin of sales, growth and customer success for academic researchers
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.
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.
Learn more
Research project 1
(Get in touch for research collaborations)
Building chemistry aware word-embeddings model
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 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?
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Research project 2
(Get in touch for research collaborations)
Building a classifier to mark words as chemical elements, properties, processes, and products
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 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?
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Research project 3
(Get in touch for research collaborations)
Document cluster summaries and labelling
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 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.)
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Research project 4
(Get in touch for research collaborations)
Explore different measures to define semantic dependencies between two texts
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?
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?
Learn more
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