Postdoc in Probabilistic Machine Learning

Postdoc in Probabilistic Machine Learning

KTH - Royal Institute of Technology

Stockholm, Sweden

Your role

The goal of this position is to conduct independent research and develop theories and methodologies that help in improving the reliability and trustworthiness of large-scale machine learning models (e.g., LLMs) in a meaningful way. You will be joining the research group of Martin Trapp and develop your own research agenda in the context of the group’s research. A particular focus of the group is to develop and utilise theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models, exact and approximate inference, and neurosymbolics. See Martin Trapp’s website for details. The candidate is expected to augment the research expertise of the group through her/his own experience and to conduct research in close collaboration with the group. You will develop theoretical and methodological work that will be released as open-source libraries and publish your work at top-tier machine learning venues (NeurIPS, ICML, ICLR, UAI, AISTATS). Moreover, you will have the opportunity to collaborate with researchers working on machine learning at KTH and collaborate with international experts through the group’s network.

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
• Research expertise in machine learning (e.g., deep learning, probabilistic methods).
• You have independence in research, curiosity, and strong critical thinking abilities.
• You have strong programming and mathematical skills.
• You have an outstanding research and publication list.
• You have well developed analytical and problem-solving skills.
• We see that you have excellent communication skills, very good knowledge of the English language both in written and oral form as this will be required in day-to-day work.

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• You have a good pedagogical ability.
• Awareness of diversity and equal treatment issues with a particular focus on gender equality.
• You are independent in your work and have no problem collaborating with others.