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
Your tasks will be an equal mix of client interaction for learning purposes, direct sales, sales strategy, contract negotiations, client delivery and future tool iteration ideation.
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?