21 of 21 Neural Network Jobs in the UK excluding London

business. You will apply the appropriate solution to the problem at hand. This could be advanced statistical techniques (Hierarchical Bayesian modelling, GAMs), AI and machine learning techniques (LLMs, neural networks) or simple arithmetic. The key is to deliver useful and timely information for the benefit of Cox Automotive Europe and the broader automotive industry. We are seeking a More ❯

deliver our projects efficiently and effectively. The Data Scientist role combines data and analytics with applications knowledge. We make extensive use of advanced techniques including LLMs, Computer Vision; Neural Networks; NLP and advanced statistics. Education Qualification (Minimum) Bachelor's Degree in a STEM subject (typically, but not limited to: Maths; Physics; Physical/Engineering Sciences; Data Science; Computer More ❯

world problems; Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning; Experience writing production-quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar; Experience with multi-GPU and distributed training at scale More ❯

engineers, and business units to deliver value. Write high-quality Python code following best practices. Continuously research and apply new tools, techniques, and technologies. Experience with: Cloud deployment,Neural networks and libraries like TensorFlow, XGBoost, CatBoost, SKlearn, API development, SQL,CI/CD, DevOps, or MLOps pipelines Experience with: Strong hands-on experience or genuine interest in data More ❯

Other services require a good working knowledge of Databricks, Logic Apps, and the Power Platform knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc) and their real-world advantages/drawbacks knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc) experience with the More ❯

these areas is essential. Essential Skills: .NET C# Mvc (Framewrk and/or Core) WebApi SignalR Python programming experience Previous experience in the development and use of Convolution Neural Networks (CNN s) for machine vision and image analysis applications Experience with large-scale data sets Experience with open source machine learning platforms such as Keras/tensorflow or More ❯

these areas is essential. Essential Skills: .NET C# Mvc (Framewrk and/or Core) WebApi SignalR Python programming experience Previous experience in the development and use of Convolution Neural Networks (CNN's) for machine vision and image analysis applications Experience with large-scale data sets Experience with open source machine learning platforms such as Keras/tensorflow or More ❯

these areas is essential. Essential Skills: .NET C# Mvc (Framewrk and/or Core) WebApi SignalR Python programming experience Previous experience in the development and use of Convolution Neural Networks (CNN’s) for machine vision and image analysis applications Experience with large-scale data sets Experience with open source machine learning platforms such as Keras/tensorflow or 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 ❯

a focus on scalability and reliability. Experience with large-scale data analysis, manipulation, and distributed computing platforms (e.g., Hive, Hadoop). Familiarity with advanced machine learning methods, including neural networks, reinforcement learning, and other cutting-edge Gen AI approaches. Skilled in API development and deployment, with a focus on performance and scalability. Interview process This interview process will More ❯

trends and issues in motor or home pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical and data science programming languages More ❯

trends and issues in motor or home pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical and data science programming languages More ❯

will play a key role within the Center of Excellence for Machine Learning. They will actively contribute to this joint development initiative, conducting in-depth analyses of complex neural networks on ARM's computing platforms and devising algorithms that will elevate us to the forefront of future Responsibilities: Develop in depth understanding of current and future ML workload More ❯

accelerator, GPU or CPU Graduate students in Computer Engineering, Electrical Engineering, Computer Science or other related technical fields Deep knowledge in machine learning, deep learning, and neural network design, optimization, and compression techniques Overall high-level knowledge of computer architecture, systems, and HW-SW co-design Ability to develop and work with large software systems in programming … help your application stand out: Experience with ML model design, optimization, and HW-SW co-development methodology ML Model Optimization techniques targeting PPA (Performance, Power, and Area) of neural networks on ARM compute platforms Adaptability to the fast-moving ML industry and willingness to learn new technology in a very dynamic environment In Return: Working on interesting new More ❯

image processing for 3D datasets, and ideally interpretation of biological (not medical) data from time series microscopy. Likely TensorFlow, Keras, PyLattice or similar. Ideally use of machine learning, neural networks, particularly CNNs given the data involved, feature extraction and tracking. Familiarity with computer vision, cellular imaging, volumetric tiling, voxel imaging, 3D graphics (rendering), spherical harmonics (3D, or 4D More ❯

image processing for 3D datasets, and ideally interpretation of biological (not medical) data from time series microscopy. Likely TensorFlow, Keras, PyLattice or similar. Ideally use of machine learning, neural networks, particularly CNNs given the data involved, feature extraction and tracking. Familiarity with computer vision, cellular imaging, volumetric tiling, voxel imaging, 3D graphics (rendering), spherical harmonics (3D, or 4D More ❯

training courses for managers. To get started, there's the 'BCAI Learning Platform', where participants can complete virtual courses on their own. These include an introduction to statistics, neural networks and time series analysis. The expert programs are particularly intensive. Michael has taken part in these. "I really like the fact that I can contribute my expertise here More ❯

underrepresentation of black women in tech. We are a non-profit organisation with the aim to grow and inspire the community of black female developers. We are the largest network of black female developers in the UK. Mission Our mission is to grow the community of black women in software development and technical roles, from entry level to executive … Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms (e.g., Regression, Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model Evaluation and Validation Big Data Technologies (e.g., Spark, Hadoop - conceptual understanding) Database Querying (e.g., SQL) Cloud-based Data Platforms (e.g., AWS Sagemaker, Google AI Platform, Azure ML More ❯

underrepresentation of black women in tech. We are a non-profit organisation with the aim to grow and inspire the community of black female developers. We are the largest network of black female developers in the UK. Mission Our mission is to grow the community of black women in software development and technical roles, from entry level to executive … Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms (e.g., Regression, Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model Evaluation and Validation Big Data Technologies (e.g., Spark, Hadoop - conceptual understanding) Database Querying (e.g., SQL) Cloud-based Data Platforms (e.g., AWS Sagemaker, Google AI Platform, Azure ML More ❯

improve their security. Extensive knowledge of AI technologies, including machine learning, deep learning, and neural networks. Solid understanding of cybersecurity principles, including encryption, authentication, access control, and network security. Experience with security frameworks and standards such as ISO 27001, NIST, and GDPR. Demonstrated experience in conducting risk assessments, threat modelling, and security audits. Excellent leadership, communication, and More ❯

improve their security. Extensive knowledge of AI technologies, including machine learning, deep learning, and neural networks. Solid understanding of cybersecurity principles, including encryption, authentication, access control, and network security. Experience with security frameworks and standards such as ISO 27001, NIST, and GDPR. Demonstrated experience in conducting risk assessments, threat modelling, and security audits. Excellent leadership, communication, and More ❯