Top Researcher in Geometric Bayesian Deep Learning
1 month ago
At Danmarks Tekniske Universitet (DTU), we are seeking a highly motivated researcher to join our team at DTU Compute. We have an exciting opportunity for a PhD scholarship in Geometric Bayesian Deep Learning. The project will focus on developing a theoretical understanding of the failure modes of Bayesian deep learning and translating these insights into efficient and scalable approximate inference techniques.
About the Project:
The goal of this project is to figure out why Bayesian deep learning doesn't work and then fix it. We aim to build highly scalable Bayesian approximations that actually work for deep learning. To achieve this, we will first identify the root cause of why the usual tricks do not work and then develop new methods to overcome these limitations.
Responsibilities:
- Conduct research on geometric Bayesian deep learning and its applications
- Develop new methods for efficient and scalable approximate inference techniques
- Collaborate with the research team at DTU Compute
Requirements:
To be eligible for this position, you must 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. You should also have prior experience with machine learning from both a theoretical and practical perspective. Experience in one of the following topics will be appreciated: approximate Bayesian inference, differential geometry, numerical computations, and large-scale computations.
We Offer:
As a researcher at DTU Compute, you will have access to state-of-the-art facilities and collaborate with an international team of researchers. Our team works in a collaborative environment, and we emphasize having fun while conducting research. We offer a competitive salary and benefits package, including a 3-year contract with negotiable starting date.
Salary:
The estimated salary for this position is around 43,000 DKK (approximately 5,700 EUR) per month, depending on your qualifications and experience.
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