26 to 50 of 83 Neural Network Jobs in the UK

other container languages A passion for applying tech to solve healthcare challenges. Non-essential skills/nice to have: Experience or understanding of Neural Networks Experience with Biotechnology/Bioinformatics Familiarity with AWS, Kubernetes, or RESTful APIs. Exposure to regulated medical software environments. If you are interested, please More ❯

other container languages A passion for applying tech to solve healthcare challenges. Non-essential skills/nice to have: Experience or understanding of Neural Networks Experience with Biotechnology/Bioinformatics Familiarity with AWS, Kubernetes, or RESTful APIs. Exposure to regulated medical software environments. If you are interested, please More ❯

Services • Experience with Computer Vision: Kernel, Hardware Accelerator, TVM, or Code-gen • Experience with Deep Learning: C++ or Python, and AI, Neural Network, Tensorflow, PyTorch, MxNET, Llvm, Compiler, CPU, CUDA, Nvidia, TensorRT, TPU, Cluster Management, High Performance Computing, or Optimization Amazon is an equal opportunities employer. We More ❯

a positive. An understanding of machine learning processes and their applications to investments. Ideally, they will be familiar with various algorithms (logistic regression, neural networks) and an understanding of how to implement these in Python. A passion for bringing together investment ideas in an organized framework with the More ❯

analysts, fostering a culture of technical excellence and innovation. Key Skills & Experience Strong expertise in statistical modelling and machine learning techniques (GLMs, GBMs, neural networks, etc.). Experience in pricing or market modelling, preferably in insurance. Proficiency in Python and SQL for data analysis and model development. Hands More ❯

general insurance 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 Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL) A good quantitative degree (Mathematics, Statistics, Engineering More ❯

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 … 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 More ❯

As part of a collaborative Computational Biology team, you will: Design and implement machine learning models—particularly language models, diffusion models, or graph neural networks —tailored to biomedical challenges. Build novel computational methods for interpreting biological sequences and structural data . Customize existing tools and develop new ones More ❯

Delivering high-quality, specification-compliant code. Utilizing tools like OpenCV, TensorFlow, and other computer vision libraries. Applying knowledge of machine learning algorithms and neural networks to enhance processing techniques. Producing high-quality design documentation that adheres to software standards for regulated industries. Demonstrating proficiency in computer vision, image More ❯

and SQL (+5 years). Proven experience in leading complex AI and machine learning projects. Advanced knowledge of statistical models (e.g., random forests, neural networks, reinforcement learning). Strong communication and stakeholder management skills. Timely delivery of high-impact data science projects. Adoption of advanced AI models across More ❯

Bonus Experience: Databricks and PySpark Analysing blockchain data Building and maintaining data pipelines Deploying machine learning models Use of graph analytics and graph neural networks Following funds on chain by using block explorers. Job Benefits Hybrid working and the option to work from almost anywhere for up to More ❯

and deployment of machine learning models into production. Proven experience with statistical methods and data science techniques: Generalised Linear Models, Gradient Boosting Algorithms, Neural Networks etc. Proficiency in writing complex SQL queries to analyse large datasets. Strong ability and experience in using Python for analytical purposes. Excellent communication More ❯

and deployment of machine learning models into production. Proven experience with statistical methods and data science techniques: Generalised Linear Models, Gradient Boosting Algorithms, Neural Networks etc. Proficiency in writing complex SQL queries to analyse large datasets. Strong ability and experience in using Python for analytical purposes. Excellent communication More ❯

and achieve acceptable performance. At Kumo, we are building a machine learning platform for data lakehouses, enabling data scientists to train powerful Graph Neural Net models directly on their relational data, with only a few lines of declarative syntax known as Predictive Query Language. The Kumo platform enables More ❯

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 … 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 More ❯

Python and potentially R. Expertise with relevant ML frameworks (e.g., scikit-learn, PyTorch). Experience with recommendation systems, collaborative filtering, and ideally Graph Neural Networks (GNNs). Proficient with SQL and working with large datasets (e.g., GCP BigQuery). Experience with cloud platforms (specifically GCP) and CI/… skills and ability to quickly adapt to new model types and requirements. Strong communication and stakeholder management skills. Expertise in advanced neural network architectures (Autoencoders, Transformers). Ability to rapidly acquire complex domain knowledge. More ❯

Python and potentially R. Expertise with relevant ML frameworks (e.g., scikit-learn, PyTorch). Experience with recommendation systems, collaborative filtering, and ideally Graph Neural Networks (GNNs). Proficient with SQL and working with large datasets (e.g., GCP BigQuery). Experience with cloud platforms (specifically GCP) and CI/… skills and ability to quickly adapt to new model types and requirements. Strong communication and stakeholder management skills. Expertise in advanced neural network architectures (Autoencoders, Transformers). Ability to rapidly acquire complex domain knowledge. More ❯

privacy services. Databricks and PySpark Analysing blockchain data Building and maintaining data pipelines Deploying machine learning models Use of graph analytics and graph neural networks If this sounds like you then please do reach out and let's have a chat More ❯

privacy services. Databricks and PySpark Analysing blockchain data Building and maintaining data pipelines Deploying machine learning models Use of graph analytics and graph neural networks If this sounds like you then please do reach out and let's have a chat More ❯

roles, supported by tailored mentoring and professional training opportunities. You will be encouraged to actively engage in industry and academic events, expanding your professional network and visibility within the quantum technology community. Our collaborative environment ensures that your contributions are recognised and that your career grows alongside our innovative … in cross-functional teams and handing off algorithms to software development teams. Nice to have experience Experience in machine learning, particularly neural network approaches to optical data analysis. Knowledge of anti-counterfeiting technologies or authentication processes. Benefits We are always listening to our team and evolving the More ❯

market data, driving business strategy and the design of our quantitative market making platform. Use advanced statistical analysis and quantitative techniques such as neural networks to build models that drive market making and risk management decisions in real time. Be involved in platform software development across a range More ❯

a strong background in machine learning, enjoy applying theory to develop real-world applications, with experience and expertise in bandit algorithms, LLMs, general neural networks, and/or other methods relevant to recommendation systems. You have hands-on experience implementing production machine learning systems at scale in Java More ❯

at the forefront of developing advanced Machine Learning solutions for autonomous driving. Our team tackles groundbreaking challenges in designing state-of-the-art neural networks, pioneering innovative end-to-end architectures, and advancing ML techniques in perception, prediction, and motion planning. We're passionate about pushing the boundaries More ❯

our recruitment team. We are looking for: We are particularly interested in candidates with experience in ONE of the following: Neuromorphic Computing, Spiking Neural Networks and event-based event monitoring/sparse data. Or: Advanced statistics and experience operationalising academic research Security clearance: You must be able to More ❯

great technical understanding of various machine learning approaches, which would include regression methods, probabilistic pricing models, tree based gradient boosting approaches, information theory, neural networks, transfer learning, etc. While your focus will be primarily on the Valuations and Pricing Team you'll also work alongside your peers in More ❯