PhD Researcher in Geometric Bayesian Deep Learning
1 month ago
We are seeking a highly motivated PhD researcher to join our team at DTU Compute, working on the intersection of Bayesian deep learning and differential geometry. The successful candidate will contribute to the development of theoretical understanding of the failure modes of Bayesian deep learning and translate these insights into efficient and scalable approximate inference techniques.
Key Responsibilities- Develop a theoretical understanding of the failure modes of Bayesian deep learning
- Translate insights into efficient and scalable approximate inference techniques
- Collaborate with a highly motivated and collaborative team
- Contribute to the development of novel Bayesian deep learning methods
- Master's degree in a relevant field (e.g., computer science, mathematics, statistics)
- Strong background in machine learning, Bayesian inference, and differential geometry
- Experience with numerical computations and large-scale computations
- Excellent communication and collaboration skills
- A highly collaborative and dynamic research environment
- Access to state-of-the-art research facilities and resources
- Opportunities for professional development and growth
- A competitive salary and benefits package
To apply, please submit your application, including a cover letter, curriculum vitae, and grade transcripts, as a single PDF file. We look forward to receiving your application
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Kongens Lyngby, Lyngby-Tårbæk Kommune, Denmark Danmarks Tekniske Universitet (DTU) Full timePhD Researcher in Geometric Bayesian Deep LearningWe are seeking a highly motivated PhD researcher to join our team at DTU Compute, working on the intersection of Bayesian deep learning and differential geometry. The successful candidate will contribute to the development of theoretical understanding of the failure modes of Bayesian deep learning and...
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Kongens Lyngby, Lyngby-Tårbæk Kommune, Denmark Danmarks Tekniske Universitet (DTU) Full timePhD Position: Geometric Bayesian Deep Learning at DTUThis PhD project focuses on developing a theoretical understanding of the failure modes of Bayesian deep learning and translating these insights into efficient and scalable approximate inference techniques.You will join a highly motivated and collaborative team under the supervision of Søren Hauberg,...
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