26 to 50 of 63 Neural Network Jobs in England

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

salaries, flexible working arrangements, and genuine job security. How It Works Step 1 – AI Engineering Fundamentals Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering. Step 2 – Data Fundamentals Understand the data workflow, from collection ...

pandas, scikit‐learn, TensorFlow/PyTorch), R, SQL, Jupyter notebooks and version control (Git). Machine Learning: regression, classification, clustering, tree‐based models, neural networks, MLOps practices and model deployment. Data Platforms: relational and NoSQL databases, time‐series stores, cloud data services (AWS Redshift, Azure Synapse, Google BigQuery ...

finance. Technical Skills Programming: Python (pandas, scikit‐learn, TensorFlow/PyTorch), R, SQL, notebooks, Git. Machine Learning: regression, classification, clustering, tree‐based models, neural networks; MLOps, model deployment, automated testing. Data Platforms: relational databases, NoSQL, time‐series, big‐data frameworks, cloud data services (AWS, Azure, GCP). Analytics ...

biological datasets (e.g. genomics, transcriptomics, proteomics) Build pipelines for processing and analysing large-scale biological data Apply deep learning techniques (e.g. transformers, graph neural networks) to biological problems Collaborate with wet-lab teams to translate experimental data into actionable insights Optimise models for performance, scalability, and real-world ...

e.g. GLM, GAMs) and deep learning techniques (e.g. neural networks, transformers, diffusion models, etc.). Comfortable designing and creating neural network architectures, tree-based methods and applying upcoming technologies from scratch. We want you to be the creator of the next generation of AI models ...

/MLOps pipelines for development and deployment Solid understanding of ML/DL fundamentals (e.g., CNNs, object detection, segmentation) Knowledge of neural network operations, linear algebra, and quantization techniques Experience optimizing neural networks for embedded systems (latency, throughput, power) Strong system-level debugging and issue ...

scalable. What you’ll do: Lead the development and refinement of novel machine learning architectures and algorithms harnessing our nonlinear dynamics. Building deeper network architectures that maximise efficiency and performance. Design, build and test models both on device and using in-house simulation framework. Collaborate closely with the wider … NeurIPS, ICML, ICLR, CVPR, or Nature/Science journals) Hardware Aware ML/Neuromorphic Computing: FPGAs, ASICs, analog computing chips, spiking neural network (hardware), edge AI Unconventional training algorithms Reservoir computing, self-constrastive learning, forward-forward learning, evolutionary algorithms, equilibrium propagation Physics informed neural networks ...

Skilled in building computer vision solutions, with a focus on object detection and motion tracking. Knowledgeable in modern machine learning techniques, including convolutional neural networks (CNNs) and transformer models. Ideally you'll have experience with tracking methods, including the use of Kalman filters. Aware of traditional, lower-resource ...

Python/TensorFlow and C# services. Design, develop, and implement advanced machine learning algorithms and models, focusing on pattern recognition and neural network applications Productionizing ML Workflows : Convert research code into robust, testable, and observable services. Implement CI/CD pipelines, automated testing, and reproducibility standards. Integration … profiling. Observability : Metrics, tracing, structured logging, dashboards. Security : SBOM, image signing, role-based access, vulnerability scanning. Qualifications Expertise in Pattern Recognition and Neural Networks Strong foundation in Computer Science and proficiency in developing scalable solutions In-depth knowledge of Statistics and advanced Algorithms Experience with tools and techniques ...

deploying machine learning methods Experience of the following predictive modelling techniques; GBMs, Elastic Net GLMs, GAMs, Logistic Regression, Random Forests, Decision Trees, Neural Nets and Clustering Experience in DevOps, or other MLOps and ML Lifecycle stacks Experience with Docker and Kubernetes Experience in creating production grade coding Experience ...

cross-functional teams to identify challenges and implement innovative solutions tailored to the organization’s needs. Qualifications Strong expertise in Pattern Recognition and Neural Networks Proficiency in Computer Science and Software Development Experience in Natural Language Processing (NLP) techniques and applications Proven problem-solving and critical thinking skills ...

motor or home pricing. Experience with some of the following predictive modelling techniques; GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering, with knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience ...