2 of 2 Reinforcement Learning Jobs in Wiltshire

ownership. As we scale, opportunities to build and lead teams. We're hiring immediately. What You'll Actually Do Derive mathematical formulations of abstract concepts. Design and implement novel learning approaches informed by physics and mathematics. Take concepts from statistical mechanics, information theory, and dynamic systems to construct first principles algorithms. Optimise using advanced mathematical structures, such as tensor … networks, and techniques like reinforcement learning - make these approaches work in a production system. Debug why your theoretically sound approach breaks at scale. Fix it. Ship it. Daily reality includes mathematical derivations and performance optimisation. You'll need to be comfortable moving between theory and systems-level engineering within the same afternoon. Required Qualifications Education & Experience: PhD in … physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning and graph neural networks, computational optimisation at scale, algorithms and data structures. More ❯

GDM Coding - Freelance (20 to 40 hours a week) Job Description: Responsibilities: Model Quality Assessment: Evaluate the quality of AI model responses that include code, machine learning, AI, identifying errors, inefficiencies, and non-compliance with established standards. Code Annotation and Labeling: Accurately generate, annotate and label code snippets, algorithms, and technical documentation according to project-specific guidelines. Review and … Kotlin Knowledge of web technologies & frameworks Web Scraping, API integration, HTML/CSS/JavaScript Web application development (e.g. Flask) Frontend (e.g. React) and backend (e.g. Node.js) development Machine Learning & Artificial Intelligence Machine Learning (General concepts, model development, experimentation, training, evaluation) Deep Learning (General, frameworks like TensorFlow, PyTorch, JAX, Keras, neural networks, CNNs, RNNs, transformer architecture, LSTM … Natural Language Processing (NLP) Reinforcement Learning (e.g., PPO, Q-learning, policy gradients, A2C, DQN, AlphaZero) Computer Vision (e.g., image processing, analysis, instance segmentation, OCR, deepfake detection More ❯