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PhD Researcher in Geometric Bayesian Deep Learning
2 months 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, particularly Bayesian deep learning
- Experience with differential geometry and numerical computations
- Excellent programming skills in Python or a similar language
- A dynamic and collaborative research environment
- Access to state-of-the-art research facilities and equipment
- 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. The application deadline is 15 September.
