26 to 29 of 29 Bayesian Methods Jobs

tools and datasets to characterise small-molecule interactions with intrinsically disordered proteins. Based just a short walk from Kings Cross station, Bind leverages interdisciplinary methods that span experimental biophysics – with a strong focus on nuclear magnetic resonance (NMR) spectroscopy – as well as computational approaches and cellular studies. You will … built models by implementing automated, streamlined, and efficient software solutions in line with best practices Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes, and make use of distributed training methodologies for large models Build model-deployment and job-launching ...

bring to the table Ability to scope and effectively deliver projects. Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. Strong problem‐solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. Excellent collaboration and communication skills … applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: operator learning (neural operators), or other probabilistic methods for PDEs; geometric deep learning or other 3D computer vision methods for point‐cloud or mesh‐structured data; generative models for geometry and ...

recognition, including Huawei’s Gold Medals and Best Technology Breakthroughs. This role focuses on advancing the state-of-the-art in reinforcement learning, Bayesian optimisation, AI agents, large language models (LLMs), and/or vision-language models (VLMs). You will work at the intersection of fundamental … eagerness to translate cutting-edge ideas into working prototypes and real-world applications. Key Responsibilities Conduct original research in reinforcement learning or Bayesian optimisation for LLMs, leading to publications in top conferences and journals (e.g., NeurIPS, ICLR, ICML). Design and implement new algorithms and models that ...

research, through to productionisation and performance monitoring Collaborate closely with data scientists and quant researchers to tackle complex mathematical and statistical problems, sharing methods and continuing the culture of ongoing research What do you need? Strong background in statistical and mathematical modelling. Ideally holding a PhD (or equivalent experience … Physics, Mathematics, Statistics or a related discipline Working knowledge of Monte Carlo simulations, Bayesian methodologies or stochastic modelling Proficient in Python as your primary modelling tool; familiarity with PySpark, Hive, Docker or Kubernetes is advantageous but not essential What's in it for you? Genuine scope ...