PhD Researcher in Bayesian Deep Learning (Remote, Denmark)

Job summary

A PhD scholarship opportunity is available focusing on large-scale Bayesian deep learning, aiming to identify and resolve the limitations of current Bayesian approaches in deep learning. The position is within a collaborative research environment that values innovative thinking and aims for high-impact publications in machine learning.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent in computer science, mathematics, or statistics.
• Prior experience in machine learning, both theoretical and practical.
• Familiarity with approximate Bayesian inference, differential geometry, numerical computations (preferably with Jax), and large-scale computations is advantageous.
• A collaborative mindset is essential.

Responsibilities

• Investigate the shortcomings of existing Bayesian deep learning methods.
• Develop scalable Bayesian approximations that effectively address identified issues.
• Collaborate with a motivated research team to contribute to high-quality publications.

Skills

• Strong analytical and problem-solving skills in machine learning.
• Proficiency in programming and numerical computation tools, particularly Jax.
• Ability to work effectively in a team-oriented environment.

Education

• A master's degree in a relevant field is required, with a focus on advanced mathematical and computational techniques.

Tools

• Experience with Jax for numerical computations is preferred, along with familiarity with machine learning frameworks and tools relevant to Bayesian methods.