1 to 25 of 81 Permanent Reinforcement Learning Jobs in the UK excluding London

Description The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm's data and analytics journey. As a part of CDAO, The Machine Learning Center of Excellence (MLCOE) partners across the firm to shape, create, and deploy Machine Learning Solutions for our most challenging business problems. This includes ensuring the quality, integrity, and … generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm's commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly. As a Summer Associate within the MLCOE, you will apply sophisticated machine learning methods … to a diverse range of complex domains, including natural language processing, large language models, speech recognition and understanding, reinforcement learning, and recommendation systems. You will collaborate closely with MLCOE mentors, business experts, and technologists, conducting independent research and deploying solutions into production. A strong passion for machine learning, solid expertise in deep learning with hands-on More ❯

and revenue growth. Under the guidance of experienced team members, you will participate in AI/ML projects across various domains, gaining hands-on experience in areas like deep learning, time series forecasting, reinforcement learning, optimization, speech and conversational AI, and Graph AI. You will also have the opportunity to support initiatives related to foundational AI models … experience, providing exposure to both technical and business aspects of AI/ML initiatives. Key Responsibilities: Assist in the development and testing of AI/ML models, including deep learning (RNN, CNN, embeddings, transformer/LLM), time series forecasting, reinforcement learning, optimization, and conversational AI. Develop robust, well-structured, and testable code to enable a seamless deployment … in Computer Science, Engineering, Data Science, or a related field. Coursework or project experience in AI/ML, including exposure to at least one of the following areas: machine learning and deep learning, time series forecasting, reinforcement learning, optimization, or conversational AI. Familiarity with Python or similar programming languages and common toolkits and AI/ML More ❯

for domain-specific applications Implement advanced prompt engineering strategies Leverage Retrieval-Augmented Generation (RAG) for enhanced contextual performance Build intelligent agents using frameworks like LangChain, LlamaIndex, CrewAI, AutoGen Apply reinforcement learning techniques including Q-learning , policy gradients , and RLlib Collaborate with cross-functional teams to integrate AI solutions into scalable products Ensure best practices in data engineering … GPT, LLaMA, Mistral, Claude) Strong background in fine-tuning and prompt engineering Hands-on experience with RAG pipelines Familiarity with Agent Frameworks (LangChain, LlamaIndex, CrewAI, AutoGen) Solid understanding of reinforcement learning concepts and tools (Q-learning, policy gradients, RLlib) Azure AI Engineer Associate certification (or willingness to obtain) Bachelor's degree in a relevant field or equivalent … with industry knowledge and technology Why Join Us? Work on impactful AI projects with real-world applications Be part of a collaborative and forward-thinking team Access to continuous learning and development opportunities Flexible working arrangements and a supportive work culture Ready to shape the future of AI? Apply now and bring your expertise to a team that values More ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯