4 of 4 Permanent Reinforcement Learning Jobs in Cambridge

ability to approach tasks in a scientific manner Data science skills, including handling of large data sets, modelling and evaluation Good understanding of software engineering concepts, particularly with machine learning development Experience with machine learning Python frameworks such as PyTorch, Tensorflow and Scikit-Learn For more senior candidates: Understanding of, including published work within, related academic fields such … as NLP, Computer Vision or Deep Learning Relevant industry experience with demonstrable impact in a commercial setting Significant practical usage of modern ML areas (NLP, Transformers, LLMs, NER, GANs, Reinforcement Learning, etc.) Requirements Master's degree or Postgraduate (preferred) qualification from a Top 200 Global University, preferably in a technical subject More ❯

in the EU Headquarters in Luxembourg (alternatively: Barcelona or Berlin or London), designed to maximize interaction with the team and stakeholders. Key job responsibilities Solve complex optimization and machine learning problems using scalable algorithmic techniques. Design and develop efficient research prototypes that address real-world problems in the massive logistics network of Amazon. Lead complex time-bound, long-term … in leadership to invest in programs of several hundreds millions euros every year. Above all, we are having fun solving real-world problems, in real-world speed, while failing & learning along the way. We use modular algorithmic designs in the domain of combinatorial optimization, solving complicated generalizations of core OR problems with the right level of decomposition, employing parallelization … and approximation algorithms. We use deep learning, bandits, and reinforcement learning to put data into the loop of decision making. We like to learn new techniques to surprise business stakeholders by making possible what they cannot anticipate. For this reason, we work closely with Amazon scholars and experts from Academic institutions. We code our prototypes to be More ❯

leader in advanced computing, with a dedicated research team focused on applying artificial intelligence to next-generation semiconductor design and optimization. Role Overview: We're seeking a motivated Deep Learning Researcher with a strong background in machine learning, AI, or related fields. You'll contribute to innovative projects in areas such as large language models (LLMs), reinforcement learning, and optimization for chip design and AI system integration. Responsibilities: Conduct and publish cutting-edge AI/ML researchDesign algorithms for chip optimization and intelligent systems Collaborate with engineering teams to integrate AI into real-world tools Stay current on AI trends and contribute to open research Requirements: PhD or equivalent experience in ML, AI, CS, physics … or mathematics Strong publication record (NeurIPS, ICML, ICLR, etc.) Proficient in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow)Solid grasp of ML techniques; independent and team-oriented mindset Preferred : Experience in LLMs, reinforcement learning, or chip designFamiliarity with JAX and optimization frameworks Why Join Us: Work on impactful AI research with real-world applicationsCollaborate with More ❯

We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC. The goal of the programme is to do … The resulting methods are also expected to have an impact in materials science, e.g, by leading to more effective batteries. The Research Assistant/Associate will join the Machine Learning Group at the Department of Engineering, working with Prof. José Miguel Hernández Lobato and Prof. Mark Girolami and other members of the Cambridge Machine Learning Group ( ). The … with other participants of the "AI Hub in Generative Models" (UCL , Oxford, Imperial, Edinburgh, Cardiff, Manchester and Surrey) and with its industrial partners. Key responsibilities include working on deep learning, probabilistic modelling, deep generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and networking with colleagues and More ❯