76 to 100 of 134 Neural Network Jobs in the UK

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 ...

deep learning frameworks (TensorFlow, PyTorch) alongside hands-on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP).Desired:Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel entities (e.g., users, destinations, itineraries, points of interest ...

engineering build-out of AI systems pioneered and prototyped by Data Science, including LLMs, retrieval augmented generation (RAG), multi-agent systems, and graph neural networks.Our Tech Stack:Our technology stack is designed to run on public cloud architectures, notably AWS and GCPDevelopment is organised around Kotlin and Python ...

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 ...

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 ...

regression/predictive modeling, causal inference, and A/B testing).Solid knowledge of core machine learning techniques (e.g., clustering, regression, decision trees, neural networks) and their real-world tradeoffs.Proficiency in Python (for statistical and ML tools) and SQL (working with large-scale databases).Comfortable researching and adopting ...

knowledge of statistical techniques: hypothesis testing, sampling theory, experimental design, and causal inference.Strong knowledge of a variety of ML techniques (e.g., clustering, regression, neural networks, etc.) and their real-world trade-offs.Expert proficiency in Python (DS/ML stack) and experience with frameworks used for LLM development ...

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 ...

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 ...

biological or chemistry software.Experience working with real-world datasets.Experience with ML on accelerators.Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RL.Culture and valuesWe are guided ...

pursuing large-scale AI research projectsAre working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of researchView research and engineering as two sides of the same coin, and seek ...

record of applying AI to real-world scientific challengesExperience with multi-parameter optimizationExperience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RLCulture and valuesWe are guided ...

linear algebra.Experience conducting hands-on analytics projects using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar methodologies. Proficiency in programming languages such as Python, Sql etc. Experience with Data visualization tools (e.g. Power BI, Tableau ...

Quantitative Trading & Market Making desk, building market making and quoting strategies across FICC products.Use advanced statistical analysis and quantitative techniques such as neural networks, machine learning, and factor models to build models that drive systematic alpha strategies which make real-time trading and risk management decisions.Implement frameworks ...

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 ...

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 ...

services lending.Familiarity with transfer learning, domain adaptation, or techniques for combining datasets of different sizes and relevance.Experience with non-linear models (gradient boosting, neural networks on tabular data) in production settings.Python proficiency – it's what the team uses day to day.The salaryWe expect to pay from ...

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 ...

deploy advanced deep learning models to tackle some of the most complex problems in global energy markets, applying cutting-edge neural network and deep learning techniques across diverse and challenging data sets.You'll combine deep technical expertise in neural networks and deep learning with practical ...

systems in early-stage startups or small greenfield teams. This is a strong positive signal. Hands-on production experience with graph neural networks, anomaly detection, or behavioural biometrics, beyond prototyping or fine-tuning. Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices. Experience mentoring ...