PhD Position in Differential Geometry and Machine Learning
18 hours ago
Introduction:
Danmarks Tekniske Universitet (DTU) invites applications for a PhD position in geometric Bayesian deep learning. Our research group focuses on developing novel methods for scalable and accurate approximate Bayesian inference in deep learning.
Research Objectives:
- To develop a theoretical understanding of the failure modes of Bayesian deep learning.
- To translate these insights into efficient and scalable approximate inference techniques.
Methodology:
The project will involve a combination of theoretical analysis, numerical simulations, and experimentation. We will leverage our expertise in differential geometry, numerical computations, and large-scale computations to design and implement novel algorithms.
Expected Outcomes:
- A comprehensive understanding of the failure modes of Bayesian deep learning.
- An efficient and scalable approximate inference technique that can be applied to various deep learning models.
About Us:
We are a highly motivated and collaborative research group within DTU Compute. Our team is led by Søren Hauberg, who has extensive experience in machine learning and deep learning research.
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