26 to 39 of 39 Bayesian Methods Jobs in the UK excluding London

team working at the intersection of artificial intelligence and quantum sensing. The positions will focus on the development and deployment of real-time AI methods for the autonomous optimisation of spin-based quantum sensors associated to solid-state defects. The successful candidates will join a research environment that … and conferences, and/or other appropriate media. Other duties Understand the intellectual property landscape relevant to the project and seek to protect the methods and techniques developed during the course of the project. Develop research objectives and proposals for own or joint research with the assistance ...

website). Key responsibilities Develop your own research program, with a view to developing new tools and techniques for probabilistic models, Bayesian optimization and related fields. Engage in team collaborations to meet research goals and report your research findings both internally and externally. Contribute to internal product … advantage: A background in decision making (Bayesian optimization, bandits, reinforcement learning, active learning) and/or probabilistic modelling and methods (Gaussian processes, Bayesian neural networks, Variational inference, etc). Experience in numerical programming (Python/NumPy/Tensorflow/PyTorch). Experience ...

array of statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records.Knowledge of Bayesian inference, high dimensional statistics, causal inference.Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience … building and running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow.Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench.Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment.Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...

statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records. Knowledge of Bayesian inference, high dimensional statistics, causal inference. Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience building and … running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow. Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench. Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment. Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...

Develop an independent research program focused on new tools and techniques for probabilistic models, Bayesian optimization, and related fields Collaborate with research teams to meet research goals Report research findings internally and externally Contribute to internal product development and customer research projects Apply cutting-edge machine learning … journals Background in decision making, including Bayesian optimization, bandits, reinforcement learning, or active learning (advantage) Background in probabilistic modelling and methods, including Gaussian processes, Bayesian neural networks, or variational inference (advantage) Experience in numerical programming with Python, NumPy, TensorFlow, or PyTorch (advantage) Experience ...

hormonal rhythms observed across individuals. To characterise this variability, you will take advantage of unique in its kind datasets and advanced mathematical and computational methods and help develop personalised models. The practical element of your project will be based on, but not limited to, time series analysis, network analysis … being welcoming and supportive. The applicant should have high proficiency with scientific computer programming in Python, Julia, R and/or MATLAB, parameter estimation methods and HPC simulations. Experience with dynamical systems theory and mathematical modelling using ODEs and/or DDEs is desirable. The vacancy ...

team working at the intersection of artificial intelligence and quantum sensing. The positions will focus on the development and deployment of real-time AI methods for the autonomous optimisation of spin qubits associated to solid-state defects. We expect the candidate to supervise on-going work on an existing … and conferences, and/or other appropriate media. Other duties Understand the intellectual property landscape relevant to the project and seek to protect the methods and techniques developed during the course of the project. Develop research objectives and proposals for own or joint research with the assistance ...

delivery teams within the UK Defence or Aerospace sectors. Experience in the practical application of mathematical techniques such as Monte Carlo simulation or Bayesian inference. Experience working with MoD assessment frameworks and an understanding of the UK Defence Science and Technology (S&T) landscape. Experience conducting peer … production environment. Experience working within secure environments and an understanding of the constraints of working with classified information. Expert knowledge of numerical methods, optimisation, and statistical analysis within a defence context Proficiency in mathematical programming using Python (NumPy, SciPy), FORTRAN, or Julia. Familiarity with simulation tools and synthetic environments ...

leave some for future years as well: – Research Fellow – Postdoc – PhD Student The work involves probabilistic modelling in exciting new settings, and developing new methods for probabilistic machine learning and inference. Applicants with outstandingly strong expertise in one of following topics are welcome, or strong expertise in one and … keen interest in working with expert colleagues on the others: automatic experimental design, Bayesian inference, human-in-the-loop learning, advanced user modelling, machine teaching, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, simulator-based inference, likelihood-free inference. There will be particularly good opportunities to join ...

Postgraduate Research Student (funded). DL June 30, 2022 I am hiring researchers to my team working on probabilistic machine learning. Keywords include: Bayesian inference, reinforcement learning and inverse reinforcement learning, automatic experimental design, multi-agent learning, Bayesian deep learning, amortized inference, human ...

each stage of the consumer’s lifecycle. Experience overseeing experimentation programmes and practices that inform product or business decisions. A good understanding of methods for statistical inference (e.g. Bayesian, Frequentist) and familiarity with predictive analytics techniques is highly beneficial. Working knowledge of common analytics tools used ...

and supervising junior researchers. You will contribute to real-time adaptive quantum sensing work using NV centres, develop Bayesian/inference methods, and #J-18808-Ljbffr ...

Watt University invites applications for a Research Associate in AI Algorithms for Spin-Based Quantum Technology. The role focuses on developing real-time AI methods to autonomously optimise spin-based quantum sensors, reinforcing the Q-BIOMED hub. The successful candidate will combine AI, Bayesian experimental design ...

Jobtailor is seeking a highly skilled ML researcher in Cambridge to lead an independent research program focused on probabilistic models, Bayesian optimization, and related fields. You will collaborate with research teams to meet ambitious goals and report findings internally and externally. You will apply cutting-edge ...