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PhD Project in Geometric Machine Learning

Technical University of Denmark - DTU Compute

Qualification Type: PhD
Location: Lyngby - Denmark
Funding for: UK Students, EU Students, International Students
Funding amount: Not Specified
Hours: Full Time
Placed On: 5th November 2019
Closes: 3rd January 2020

DTU Compute’s Section for Cognitive Systems, would like to invite applications for a 3-year PhD position starting March 2020. The project is financed by the European Research Council, through a Starting Grant.

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.

Project Description

The PhD 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.

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.

Qualifications

Candidates should have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. The master degree should be in computational science and engineering (CSE), applied mathematics, or engineering, or equivalent academic qualifications.

Preference will be given to candidates who can document experience with machine learning, and in addition have an interest in basic research. Experience with Riemannian geometry is a benefit, but not a requirement. Furthermore, good command of the English language is essential.

Approval and Enrolment

The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in the DTU Compute PhD School Programme. For information about the general requirements for enrolment and the general planning of the PhD study programme, please see the DTU PhD Guide.

Assessment

The assessment of the applicants will be made by Associate professor Søren Hauberg.

We offer

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.

Application procedure

To apply, please read the full job advertisement at www.career.dtu.dk

Application deadline: 3 January 2020 

   
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