|Location:||Lyngby - Denmark|
|Placed On:||5th November 2019|
|Closes:||3rd January 2020|
DTU Compute invites applicants for a two-year postdoc position in Operational Representation Learning.
The Section for Cognitive Systems is an internationally renowned group for machine learning research. The group aims for the highest quality research. You are encouraged to collaborate both within the group and with other international groups. We emphasize a healthy work/life balance based on the premise that you do the best work when you are happy.
The project revolve around the goal of learning operational representations, i.e. representations that are naturally equipped with a set of well-defined operations that may be performed. For example, we may seek a representation that supports operators akin to addition and subtraction, or we may seek a representation that naturally supports integration (in order to assign probabilities to events). In practice, the project will focus on building both practical tools as well as theoretical foundations for working with random Riemannian representations, which naturally appear in many generative models. For more details see Operational Representation Learning.
Responsibilities and tasks
Most learned representations are treated as being Euclidean even if it is trivial to construct counter-examples showing that the Euclidean assumption lead to arbitrariness. You will join a team of people dedicated to avoiding this arbitrariness. You will work with nonlinear generative models and use geometric techniques to develop well-defined operations that can be meaningfully applied in the representation space of the model. The end-goal is to both improve the modelling capacity of generative models, but also to improve their general interpretability.
Depending on interest and qualifications of the applicant, the project can either be theoretical, applied, or a combination thereof. We generally believe that theory and applications must go hand in hand to ensure that the theory is meaningful and beneficial to scientific discovery.
More details are available at Open positions.
You should have a PhD degree or equivalent in machine learning or a related field. You are expected to have both programming experience and be comfortable manipulating mathematical and probabilistic models. You will be working with a diverse group of people, so “people skills” are considered important.
Preference will be given to candidates with a publication record at top machine learning venues such as NeurIPS, ICML, ICLR, AISTATS, UAI, and similar.
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.
To apply, please read the full job advertisement at www.career.dtu.dk
Application deadline: 3 January 2020
Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 11,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. Our main campus is in Kgs. Lyngby north of Copenhagen and we have campuses in Roskilde and Ballerup.
Type / Role: