PhD scholarship in Uncertainty Quantification for Deep Learning | Kongens Lyngby, Lyngby‐Taarbæk Municipality, Denmark | Technical University of Denmark

Position:	PhD scholarship in Uncertainty Quantification for Deep Learning
Institution:	Technical University of Denmark
Location:	Kongens Lyngby, Lyngby‐Taarbæk Municipality, Denmark
Duties:	Develop novel methods for uncertainty quantification in deep learning; Work with state-of-the-art nerual network architectures applied to molecular data; Publish scientific papers and present research results in top machine learning conferences such as NeurIPS, ICML, UAI, and AISTATS; Assist in machine learning teaching and supervision
Requirements:	You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree; Proven experience in Bayesian methods, probabilistic modeling, and probability theory; A strong grasp of the theoretical foundations and practical implementation of Markov chain Monte Carlo (MCMC) methods; Proven experience with implementing machine learning methods in Python and Pytorch/Tensorflow

Text:	PhD scholarship in Uncertainty Quantification for Deep Learning Develop novel methods for uncertainty quantification in deep learning; Work with state-of-the-art nerual network architectures applied to molecular data; Publish scientific papers and present research results in top machine learning conferences such as NeurIPS, ICML, UAI, and AISTATS; Assist in machine learning teaching and supervision You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree; Proven experience in Bayesian methods, probabilistic modeling, and probability theory; A strong grasp of the theoretical foundations and practical implementation of Markov chain Monte Carlo (MCMC) methods; Proven experience with implementing machine learning methods in Python and Pytorch/Tensorflow