1 to 25 of 64 Permanent Neural Network Jobs

We are looking for a goal oriented and driven AI/ML Engineers with relevant experiences. Key Responsibilities: Neural Network Training : Develop and train advanced neural network models to meet specific functional requirements. Deployment and Integration : Efficiently deploy these neural networks on … trends. Skills and Qualifications: Minimum 2 years of experience in building AI/ML software. Strong expertise in machine learning and neural network algorithms. Proficient in programming languages like Python, and frameworks such as TensorFlow or PyTorch. Experience with server deployment, cloud computing environments, and API development. more »

AI) development and optimisation. As part of a team, you will be working on developing software tools and libraries to optimise neural network performance collaborating with hardware engineers, architects, and other developers. You will collaborate to push the limits of processor capabilities and develop software to leverage … these. You will gain an understanding of Neural network models and algorithms to optimise and improve performance and integration. They are looking for someone with a strong programming background in Python & C/C++ with experience of software optimisation techniques including parallelisation, vectorisation, and memory optimisation. You … will a solid understanding of neural network architecture and algorithms. They are looking for someone with a strong academic background coupled with solid industry experience within a challenging technical environment. You will need strong communication skills including good written and verbal experience as well as ideally some more »

Performance Tools Engineer in Cambridge, UK About Neubla As pioneers of innovative neural computing, Neubla is transforming the future of computing by bringing the enormous potential of neural computing to the highest performance in most data-intensive applications. Neubla is made up of people with expertise … CPU. However, in AI accelerator domain, the performance model has often been taken lightly due to the regular computation patterns of neural network models. However neural network models are getting bigger and much more complex every month, and it became almost impossible to estimate more »

client delivers systems that can perceive and understand the world with the same semantic richness as the human brain and using the same neural mechanisms they believe are at work in the brain. Their approaches are inspired by their recent advances in theoretical neuroscience, which are beginning to … how the brain makes sense of the world. The project team will work on the development of a novel enhanced convolutional neural network based on a proprietary approach already developed by the company. Applied Computer Vision Data Scientist - job responsibilities: Enhance our machine learning software with the …/computer vision algorithms. Apply existing knowledge of Transformers and CNNs, machine vision & image analysis techniques to the development of enhanced neural network systems based on our research outputs. Work to customise and implement our machine learning solutions for use by prestigious and demanding customers. Contribute to more »

consultative approach to distil complex requirements into robust data science models such as regression and propensity models, time-series modelling, ensemble methods, or neural networks for optimal decision-making. Spearhead initiatives with cross-functional stakeholders. ● Opportunity Identification: Partner with business stakeholders to unearth untapped data science applications utilising more »

consultative approach to distil complex requirements into robust data science models such as regression and propensity models, time-series modelling, ensemble methods, or neural networks for optimal decision-making. Spearhead initiatives with cross-functional stakeholders. ● Opportunity Identification: Partner with business stakeholders to unearth untapped data science applications utilising more »

consultative approach to distil complex requirements into robust data science models such as regression and propensity models, time-series modelling, ensemble methods, or neural networks for optimal decision-making. Spearhead initiatives with cross-functional stakeholders. ● Opportunity Identification: Partner with business stakeholders to unearth untapped data science applications utilising more »

cluster computing and containerization Experience working in parallel/scalable languages such as Spark or Dask Experience working with knowledge graphs and graph neural networks Biotech or biopharma experience, ideally within R&D Experience in a startup or scale-up environment Dewpoint Therapeutics is the first company founded more »

Science, Mathematics, Statistics, Physics, Engineering, or a related field. Strong background in deep learning, with hands-on experience in developing and implementing deep neural networks using frameworks such as TensorFlow, PyTorch, or Keras. Proficiency in programming languages such as Python and experience with data manipulation and analysis libraries more »

with data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy). Strong knowledge of machine learning techniques and algorithms (e.g., regression, classification, clustering, neural networks). Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau) and proficiency in SQL for data extraction and manipulation. Excellent problem-solving skills more »

testing, and regression. Skilled in employing advanced machine learning algorithms and statistical techniques like regression, simulation, scenario analysis, modelling, clustering, decision trees, and neural networks to address business challenges. Demonstrated track record of applying statistical modelling and machine learning in industrial settings. Experience mentoring junior team members If more »

Science or Robotics. Relevant publications or talks at industry conferences or in journals. Developed machine learning applications in Python. Experience with compilers for neural networks. Experience developing and fielding autonomous systems. Familiarity with Google Cloud Platform. The Candidate Journey: Multi-Step and Two-Way No-one wants to more »

selection, PCA, hypothesis testing, time series, queuing theory, survival analysis, clustering, linear programming. Experience with machine learning methods, such as regularization, random forests, neural networks and deep learning. Ability to write algorithms and implement pipelines in Python. Knowledge of Scala, R, is a plus. Experienced in SQL. Familiarity more »

and industry for competitive research and development opportunities. Practical understanding of generative models Experience programming machine learning algorithms for GPUs Understanding of Convolutional Neural Nets Experience adapting large language models technologies Discernment of when and how to use machine learning regulation Experience adapting ML algorithms and tools with more »

such as numpy, pandas, and scikit-learn Experience with deep learning frameworks (PyTorch, JAX, Keras, Tensorflow) Strong theoretical understanding of machine learning and neural networks Experience with containerized processes (Docker, Kubernetes) Familiarity with cloud services and distributed computing frameworks (AWS, PySpark, Ray Distributed) Problem-solving aptitude and creative more »

Data Scientist that wants to design and push boundaries in the Remote Sensing and Environmental space using Python, Deep learning/ML and Neural networks and ideally some experience practically developing learning in Computer Vision Our clients mission is to empowers efforts to heal ecosystems globally. We champion more »

Proficiency in Python Strong NLP experience – NumPy, Pandas etc. Commercial experience leveraging open-source models, finetuning LLMs & RAG pipelines Expertise in learning algorithms, neural networks and ML frameworks (TensorFlow, PyTorch etc.) MLOps experience Nice to have: Familiarity with Git or other Version Control Systems Computer Vision Library exposure more »

features, NumPy, keras, pandas, NLTK, scikit, SpaCy > Machine Learning NLP, ML algos, Hugging Face, large language models (LLMs), Retrieval Augmented Generation), learning algorithms, neural networks, ML frameworks (TensorFlow, PyTorch) > Strong IaC experience, using Terraform or CloudFormation > Data Wrangling > Systems: Linux, APIs, infrastructure design load balancing, VMs, PostgreSQL, vector more »

or home 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. Knowledge of the technical differences between different packages for some of these model types would be an advantage. Experience in statistical more »

The Role: Your day-to-day responsibilities will include: Working on core segmentation, churn, and recommender system-based projects, alongside deep learning and neural networks to support core clients Part of a team of 8 reporting to the Head of Data Science - working closely with a wider Engineering more »

and industry-leading computing tools to apply and build ML models from the ground up, utilizing new and established techniques and methods like neural networks, reinforcement learning, deep learning, and natural language processing. Remain connected to the broader research community, staying on top of the latest research and more »

features, NumPy, keras, pandas, NLTK, scikit, SpaCy Machine Learning – NLP, ML algos, Hugging Face, large language models (LLMs), Retrieval Augmented Generation), learning algorithms, neural networks, ML frameworks (TensorFlow, PyTorch) Data Wrangling Systems: Linux, APIs, infrastructure design – load balancing, VMs, PostgreSQL, vector dbs. ML Learning Engineer – desirable skills: Version more »

features, NumPy, keras, pandas, NLTK, scikit, SpaCy Machine Learning NLP, ML algos, Hugging Face, large language models (LLMs), Retrieval Augmented Generation), learning algorithms, neural networks, ML frameworks (TensorFlow, PyTorch) Data Wrangling Systems: Linux, APIs, infrastructure design load balancing, VMs, PostgreSQL, vector dbs. ML Learning Engineer desirable skills: Version more »

features, NumPy, keras, pandas, NLTK, scikit, SpaCy Machine Learning – NLP, ML algos, Hugging Face, large language models (LLMs), Retrieval Augmented Generation), learning algorithms, neural networks, ML frameworks (TensorFlow, PyTorch) Data Wrangling Systems: Linux, APIs, infrastructure design – load balancing, VMs, PostgreSQL, vector dbs. ML Learning Engineer – desirable skills: Version more »

Engineer Strong foundation in Python Excellent knowledge of ML libraries and frameworks like TensorFlow, PyTorch, Keras and scikit-learn. Experience with neural network architectures, deep learning techniques, and building and training deep learning models. Creating and handling large datasets including pre-processing steps and familiarity with data more »