51 to 75 of 85 Neural Network Jobs in the UK excluding London

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 ...

designing, building, and deploying production-grade AI and Machine Learning solutions at scale.Deep expertise in LLMs, Generative AI, RAG, Agentic AI, Deep Learning, Neural Networks, and Transformer architectures.Hands-on experience with LLM evaluation, benchmark design, model validation, prompt engineering, guardrails, and AI quality assessment.Good foundation in statistics, probability ...

with 1 years working experience.Programming Language: Proficiency in Python.Solid understanding of data structures and algorithms.Proficient in Machine Learning, with a solid understanding of neural networks and the Transformer architecture. Familiar with foundational NLP tasks such as tokenization, Named Entity Recognition, and sentiment analysis.Experience with modern approaches built ...

strategies.Knowledge of generative AI architectural patterns including RAG & fine tuning.Experience training & deploying machine learning models using a range of techniques (e.g. Classification, Regression, Neural Networks, etc).Experience with developing and deploying applications in cloud environments, such as AWS, Azure, or Google Cloud Platform.Understanding of how to design ...

practices, API design, distributed computing, UNIX tooling, and scalable system design. Machine Learning and AI Fundamentals - Solid knowledge of supervised and unsupervised learning, neural networks, model evaluation, prompt engineering, code transformation and refactoring, data transformation and schema conversion, automated test case generation, and responsible AI practices. Data Handling ...

applicationsExperience with model performance evaluation and predictive model optimisation for accuracy and efficiencyWhat could set you apartExperience with specific AI techniques, such as neural networks or natural language processing.Knowledge of the financial services industry.Contributions to open-source AI projects.Experience with data visualization tools.Passion for data science, data mining ...

applicationsExperience with model performance evaluation and predictive model optimisation for accuracy and efficiencyWhat could set you apartExperience with specific AI techniques, such as neural networks or natural language processing.Knowledge of the financial services industry.Contributions to open-source AI projects.Experience with data visualization tools.Passion for data science, data mining ...

understanding of best practices in software engineering and data engineeringIn-depth knowledge of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etcSolid knowledge of statistics: hypothesis testing, confidence intervals, bootstrapExperience of end-to-end model development and maintenance of ML models used ...

Scikit-learn, PyTorch, or TensorFlow. Experience with cloud platforms or distributed computing (GCP preferred, but AWS or Azure also welcome). Exposure to neural networks (a plus, not a must). A genuine interest in sports and data-driven decision making. What you’ll get in return Fully ...

infrastructure-as-code tool. Docker, CI/CD and Git-based development. API monitoring, logging and production support. Working with models such as neural networks and random forests. Financial services or insurance experience would be useful, but it isn't essential. Strong ML and software engineering fundamentals matter ...

infrastructure-as-code tool. Docker, CI/CD and Git-based development. API monitoring, logging and production support. Working with models such as neural networks and random forests. Financial services or insurance experience would be useful, but it isn't essential. Strong ML and software engineering fundamentals matter ...

well as models.Train Machine Learning Models: Use machine learning and statistical modelling techniques such as recommendations, reinforcement learning, decision trees, Bayesian analysis, neural networks and transformers to develop and evaluate algorithms to address business use cases and/or to improve product/system performance, quality and accuracy.Near ...