2 of 2 Remote Random Forest Jobs in the East of England

and specific architectures (Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and attention-based networks) Strong understanding and experience with machine learning algorithms (such as random forests, SVMs, boosting, and neural networks) along with optimization techniques. Proficiency in statistical analysis and data handling, including data augmentation, data cleaning, normalization, and More ❯

estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster-Shafer theory of evidence Machine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent … variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clustering Deep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search, deep regression/classification, deep embeddings, recurrent Networks, natural language processing Computer Vision algorithms e.g. Structure from motion, image Based navigation More ❯