Tenure Track Assistant Professor in Probabilistic Modelling

This tenure track assistant professor position in probabilistic modelling requires strong expertise in probability theory, statistical inference, and stochastic processes. Candidates should have a solid foundation in mathematical and computational methods for modelling uncertainty and randomness in complex systems. Proficiency in probabilistic programming languages and frameworks, such as PyMC, Stan, or TensorFlow Probability, is highly valuable. Experience with Bayesian methods, Markov chain Monte Carlo techniques, and variational inference is often essential. The role demands the ability to conduct original research, publish in reputable academic journals, and contribute to the advancement of probabilistic modelling theory and applications. Teaching responsibilities typically include undergraduate and graduate courses in probability, statistics, and machine learning, requiring excellent communication and pedagogical skills. Collaboration with interdisciplinary teams and the ability to secure research funding through grant proposals are important. Candidates are expected to have a PhD in statistics, applied mathematics, computer science, or a related field, demonstrating rigorous academic training and research competence. While specific experience levels are not provided, the tenure track nature of the role implies readiness to establish an independent research program and contribute to academic service activities. Overall, the position demands a strong blend of theoretical knowledge, practical modelling skills, and academic leadership potential in the field of probabilistic modelling.