1 to 25 of 118 Neural Network Jobs in England

Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D. VCG's mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency. What you … least 1 research publication in a top-tier conference like CVPR, ECCV, ICCV, SIGGRAPH or similar Solid background in one or more of the following: (i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design; (ii) design and test of advanced convolutional … recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner; (iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar Desired qualifications: Publications, e.g. in top-tier conferences and journals: IEEE Transactions More ❯

centers in Cambridge, London, Edinburgh and Ipswich. We continue to explore and define new research directions and new services. We have expanded our collaborations with academic researchers; researched new network architectures, integration of communications and key enabling technologies; and developed the fundamental theories of these technologies. We invite you to join us on this exciting journey and drive your … This is not a maintenance role; it is a ground-zero opportunity to architect the low-level software stack for a heterogeneous computing platform where the GPU, NPU (Neural Processing Unit) and other modules are designed for deep, synergistic cooperation. Your work will be critical to unlocking the full potential of neural rendering, machine learning and … AI workloads. Performance Analysis & Optimization: Attain unparalleled performance by deep-diving into the hardware pipeline. Identify and eliminate bottlenecks in the interaction between graphics shaders and neural network execution. Define the Programming Model: Create and document the programming models, APIs, and developer tools that will allow internal and external graphics engineers to leverage the combined power of More ❯

and experimentation. You’ll work within multidisciplinary teams to develop, validate and deploy machine learning models to meet challenging customer requirements. Key Responsibilities Develop and train neural network models using frameworks such as PyTorch and TensorFlow Select and adapt model architectures to meet specific project requirements Build, curate, and manage training datasets, including data augmentation, feature extraction … technical and non-technical audiences Requirements Degree in Computer Science, Engineering, Mathematics, or related field Strong development skills in Python and C/C++ Experience with neural network architectures including RNNs, transformers, and vector quantisation In-depth knowledge of machine learning architectures and training algorithms Experience in model training, quantisation, and conversion for inference Hands-on experience More ❯

and experimentation. You’ll work within multidisciplinary teams to develop, validate and deploy machine learning models to meet challenging customer requirements. Key Responsibilities Develop and train neural network models using frameworks such as PyTorch and TensorFlow Select and adapt model architectures to meet specific project requirements Build, curate, and manage training datasets, including data augmentation, feature extraction … technical and non-technical audiences Requirements Degree in Computer Science, Engineering, Mathematics, or related field Strong development skills in Python and C/C++ Experience with neural network architectures including RNNs, transformers, and vector quantisation In-depth knowledge of machine learning architectures and training algorithms Experience in model training, quantisation, and conversion for inference Hands-on experience More ❯

Requirements Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science , or a related technical field. Strong understanding of core ML concepts (supervised/unsupervised learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas More ❯

Requirements Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science , or a related technical field. Strong understanding of core ML concepts (supervised/unsupervised learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas More ❯

Requirements Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science , or a related technical field. Strong understanding of core ML concepts (supervised/unsupervised learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

and work collaboratively in a team environment Nice to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real More ❯

and work collaboratively in a team environment Nice to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real More ❯

and work collaboratively in a team environment Nice to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real More ❯

with engineering and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms More ❯

with engineering and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms More ❯

with engineering and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms More ❯

with engineering and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms More ❯

with engineering and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms More ❯

R, Kubernetes, Kubeflow, JavaScript/JVM, Kafka Background in Application development, deployment, and monitoring on Kubernetes Proficient in PyTorch or similar frameworks (e.g Tensorflow) – demonstrates strong understanding of neural networks and LLMs; familiar with frameworks widely used in Hugging Face models, LLaMA, and similar architectures Expertise in fine-tuning methodologies – including data collection strategies for effective model adaptation More ❯

R, Kubernetes, Kubeflow, JavaScript/JVM, Kafka Background in Application development, deployment, and monitoring on Kubernetes Proficient in PyTorch or similar frameworks (e.g Tensorflow) – demonstrates strong understanding of neural networks and LLMs; familiar with frameworks widely used in Hugging Face models, LLaMA, and similar architectures Expertise in fine-tuning methodologies – including data collection strategies for effective model adaptation More ❯

R, Kubernetes, Kubeflow, JavaScript/JVM, Kafka Background in Application development, deployment, and monitoring on Kubernetes Proficient in PyTorch or similar frameworks (e.g Tensorflow) – demonstrates strong understanding of neural networks and LLMs; familiar with frameworks widely used in Hugging Face models, LLaMA, and similar architectures Expertise in fine-tuning methodologies – including data collection strategies for effective model adaptation More ❯