5 of 5 Permanent Bayesian Methods Jobs in the East of England

Self-motivated, enthusiastic, and organised, with excellent attention to detail Proactive and delivers to timescales Desirable Attention to detail in creating documentation The ability to conduct descriptive statistics, Bayesian statistics, Inferential statistics, Probability distributions and theory, dimensionality reduction and sampling etc Strong numerical and statistical skills Additional Requirements Essential Willing to be flexible in working practices Improvement More ❯

please apply. Responsibilities Design, build and scale supervised ML models for active learning and Bayesian Optimization of materials synthesis and performance Implement best practices and innovate methods for uncertainty quantification Combine datasets of multiple fidelities and sources to power data-driven materials discovery Work with the computational team to identify materials design pathways that target desired … conferences, and developing relationships with key opinion leaders Report findings to stakeholders and leadership in written reports and verbal presentations. Qualifications Experience with uncertainty quantification, active learning and Bayesian Optimization Experience implementing, evaluating, and hyperparameter tuning small and large supervised models in a Bayesian Optimization context (Gaussian processes, Bayesian Neural Networks More ❯

Who You Are - Proven track record leveraging machine learning to solve real-world problems; Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning; Experience writing production-quality code with modern machine learning frameworks such as PyTorch … Experience in cell health and rejuvenation-related research area; Experience with identification and assessment of drug targets and/or therapeutic compounds; Experience in the application of machine learning methods to biological data, including genomics, transcriptomics, epigenetics, proteomics, and imaging; Track record in open-source software development, e.g., demonstrated by high-impact GitHub repository; Track record of high-caliber More ❯

maintenance. The ideal candidate will be a good communicator and have a growth mindset with an enthusiasm for scientific discovery and strong interest in applying a combination of AI methods and mathematical models toward understanding mechanistic aspects of cellular processes with all experimental labs at Altos Labs. The ideal candidate is particularly interested in multi-scale (systems) biochemistry and … loop between experimental and theoretical work, at multiple scales, from molecules to cells, tissues and even whole organisms. Working at the interface between mathematical and computational models, and AI methods, with the aim of establishing design principles of rejuvenated cells. Collaborating with both experimental and computational scientists across Altos. Influence best practices in areas such as Bayesian … Biology, Computational Biology, Computer Science, or closely related field with strong emphasis in biological modeling. Relevant industry and/or academic experience. Expertise and a track record of using methods from artificial intelligence for biological design. Record of applications of dynamical systems to problems of synthetic biology. Record of applications of data driven modeling methods and AI to More ❯

Postdoc in Bayesian machine learning, AstraZeneca, Cambridge, UK Mar 29, 2018 PREDICTING DRUG TOXICITY WITH BAYESIAN MACHINE LEARNING MODELS We're currently looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Cambridge, UK you'll be in a global pharmaceutical environment, contributing to live projects right from the … advisor, who'll provide you with the guidance and knowledge you need to develop your career. You will be part of the Quantitative Biology group and develop comprehensive Bayesian machine learning models for predicting drug toxicity in liver, heart, and other organs. This includes predicting the mechanism as well as the probability of toxicity by incorporating scientific … will be used to account for uncertainty in the inputs and propagate this uncertainty into the predictions. In addition, you will promote the use of Bayesian methods across safety pharmacology and biology more generally. You are also expected to present your findings at key conferences and in leading publications This project is in collaboration with Prof. More ❯