3 of 3 Permanent Neural Network Jobs in Oxford

with a deep-tech start-up seeking a highly skilled AI engineer to join their growing team. This role will focus on developing AI algorithms for Neural Network-based NLP and Computer Vision, while collaborating with institutional, academic, and commercial partners. The Role Develop and refine AI models for Natural Language Processing (NLP) and Computer Vision Adapt … Key Requirements Active SC Clearance is required PhD or MSc in Computer Science, Electrical Engineering, Mathematics, or a related field, or equivalent industry experience Expertise in Neural Network-based NLP and Computer Vision Strong programming skills in Python Proven ability to work collaboratively within a research team Strong analytical and problem-solving skills Preferred Skills PhD in More ❯

with a deep-tech start-up seeking a highly skilled AI engineer to join their growing team. This role will focus on developing AI algorithms for Neural Network-based NLP and Computer Vision, while collaborating with institutional, academic, and commercial partners. The Role Develop and refine AI models for Natural Language Processing (NLP) and Computer Vision Adapt … Key Requirements Active SC Clearance is required PhD or MSc in Computer Science, Electrical Engineering, Mathematics, or a related field, or equivalent industry experience Expertise in Neural Network-based NLP and Computer Vision Strong programming skills in Python Proven ability to work collaboratively within a research team Strong analytical and problem-solving skills Preferred Skills PhD in More ❯

and deep learning. On the one hand, Bayesian learning provides a theoretically sound framework to formalise the estimation of the architecture and the parameters of deep neural network models. On the other hand, deep learning offers new tools in Bayesian modelling, e.g. to learn flexible nonparametric priors or computationally efficient posterior distribution approximations. State of the art … The field of machine learning has recently been much impacted by deep learning. Deep neural networks are now at the basis of the state-of-the-art in computer vision, natural language processing, to cite just a few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be … grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At the same time, the theory at the basis of deep neural networks is not yet very well understood and its grounds must be laid out. Although the interaction between these two learning paradigms is relatively under-explored, there is a More ❯