7 of 7 Neural Network Jobs in the Thames Valley

pipelines (realtime or batch) & data quality using modern toolchain (e.g., Apache Spark, Kafka, Airflow, dbt). Strong foundational knowledge of machine learning and deep learning algorithms, including deep neural networks, supervised/unsupervised learning, predictive analysis, and forecasting. Expert-level proficiency in Python, with a demonstrated ability to develop and debug production-grade code. Desired Skills (Bonus Points 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 ❯

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 ❯

Required I'm working with a deep-tech start-up looking for an experienced AI Engineer to join their expanding team. This role focuses on developing Neural Network-based NLP and Computer Vision algorithms, working alongside institutional, academic, and commercial partners. The Role • Develop/refine AI models for NLP • Design new scientific methods & experimental protocols • Analyze … meetings Key Requirements • Active SC Clearance (or eligible for SC Clearance) • MSc/PhD in CS, Engineering, Maths or relevant field (or equivalent experience) • Expertise in Neural Network-based NLP & LLM • Strong Python skills • Collaborative, analytical, and problem-solving mindset Gen AI Engineer – Hybrid (Oxford 2 Days/Week) – £100,000+ – SC Clearance Required More ❯

Computer Science, Machine Learning, or a closely related field Strong foundation in machine learning and deep learning algorithms (e.g., transformers, GNNs, supervised/unsupervised learning, reinforcement learning, deep neural networks) Excellent Python programming skills with experience in developing and debugging production-level code Desired Skills (Bonus Points): Proven experience in recommender systems, behavioural AI, and/or reinforcement 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 ❯

projects through to delivery. Ideally, experience applying AI in a commercial or customer-focused environment. Hands-on knowledge in areas such as signal processing, reinforcement learning, neural network training, or multi-agent systems. Familiarity with research publishing processes and participation in academic or industry conferences. Responsibilities Leading research initiatives that explore the practical application of AI within More ❯