126 to 147 of 147 Bayesian Methods Jobs

and engineers to build AI systems tailored for biomedical discovery Contribute to long-term research programs focused on autonomous AI agents Lead and implement state-of-the-art ML methods into a therapeutic platform Publish high-impact research and support the development of patents Your Experience MSc (or equivalent) in Machine Learning, Computer Science, or related field Proven track … record with LLMs, generative AI, optimization, or sequential learning Experience with deep learning frameworks (TensorFlow, PyTorch, JAX, etc.) Expertise in one or more: reinforcement learning, Bayesian optimization, optimal experimental design, agents, or causality Experience in drug/target discovery, treatment modeling, or medical AI research Publications in high-impact conferences or journals highly desirable Next Steps This More ❯

firm with startup-style freedom to build sports models. Role Overview Develop American football, baseball, tennis & ice hockey models & more Price same-game parlays using advanced stats Apply Bayesian inference, filtering & mathematical modeling Work with quant engineers on dashboards & implementation Ideal Candidate Strong quant background (Bachelor’s/Master’s; PhD a plus) Experience in syndicate or More ❯

firm with startup-style freedom to build sports models. Role Overview Develop American football, baseball, tennis & ice hockey models & more Price same-game parlays using advanced stats Apply Bayesian inference, filtering & mathematical modeling Work with quant engineers on dashboards & implementation Ideal Candidate Strong quant background (Bachelor’s/Master’s; PhD a plus) Experience in syndicate or More ❯

growing team focused on quantum sensing for a variety of industry sectors such as aerospace, defence, geophysical exploration and Earth observation. Perform essential research and development into performance-enhancing methods for next-generation fielded quantum magnetometers. Develop novel theoretical models and numerical simulation tools suitable for assessing the performance of quantum sensors in real-world environments. Develop platform and … discipline. Experience in the theory, numerical modelling, and/or optimization of one or more of the following disciplines: quantum atomic physics; quantum magnetometry; quantum control; real-time Bayesian estimation (e.g. particle filtering). Finally, you will have a strong desire to work with a world leading team and a company that is fundamentally building the future … quantum technology industry. It would be fantastic if you have these skills but not essential: Familiarity with the Python programming language for scientific computing. Experience in modern signal processing methods (e.g. Bayesian methods, particle filtering, sequential Monte Carlo methods); Experience in sensor modelling and sensor signal processing, including data fusion;Experience modelling optically-pumped More ❯

Analysts, and Marketers. Mission: Contribute to making money without borders the new normal. Qualifications: Experience with LTV modeling, econometrics, and marketing mix modeling. Knowledge of Bayesian methods, neural networks (preferably PyTorch). Strong statistical background, especially Bayesian reasoning. Experience with causal inference models. Proficiency in various modeling techniques: gradient boosting, neural networks, linear More ❯

fill most positions now but 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 new work on collaborative modelling and decision-making with AI. And applications in drug design More ❯

using these insights to create best practices for testing exercises. Developing our approach to uncertainty quantification and significance testing, increasing statistical power (given time and token constraints). Developing methods for inferring model capabilities across given domains from task or benchmark success rates, and assigning confidence levels to claims about capabilities. Predictive Evaluations: The goal is to develop approaches … methodologies, potentially in fields outside of machine learning, and statistical analysis (T-shaped: some deep knowledge, lots of shallow knowledge, in e.g. experimental design, A/B testing, Bayesian inference, model selection, hypothesis testing, significance testing). Deeply care about methodological and statistical rigor, balanced with pragmatism, and willingness to get into the weeds. Experience with data More ❯

importantly, impact patients' lives. Opportunity We're now hiring a Director of Statistical Genetics & Functional Genomics to lead a growing team that integrates human genetics, functional genomics, and computational methods to drive novel target discovery. This is a rare opportunity to help shape our therapeutic pipeline by unlocking the causal mechanisms of disease from human data. Your responsibilities Lead … architecture. Collaborate cross-functionally with disease biology, target discovery, and experimental teams to translate genetic insights into testable hypotheses and novel therapeutic concepts. Stay at the forefront of emerging methods in statistical genetics and variant-to-function inference, guiding internal adoption and development. Contribute to platform strategy and target identification efforts across multiple disease areas. Foster a culture of … and supporting their continued development. Who you are You bring deep expertise in human statistical genetics, including fine-mapping, LD structure, polygenic risk scoring, partitioned heritability, and causal inference methods (e.g. Mendelian Randomisation). You have experience integrating multi-modal data-including GWAS, transcriptomic (bulk and single-cell), epigenomic (ATAC-seq, ChIP-seq), and proteomic datasets-to identify causal More ❯

testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯

testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯

travel to company events 2-4 times a year, for up to two weeks each. Other skills you may bring Experience in statistical significance testing and experience in Bayesian inference and/or predictive analytics and ML. Python/R/SQL. Exposure to the following tools: LinkedIn Talent Insights, Greenhouse, DISC profiling. What we offer you More ❯

General Theory for Big Bayes" and Grenoble IDEX. Interested applicants should write to us with: a letter of interest, CV, and should require two recommendation letters. Context Bayesian deep learning brings together two of the most important machine learning paradigms: Bayesian inference and deep learning. On the one hand, Bayesian learning … theoretically sound framework to formalise the estimation of the architecture and the parameters of deep neural network models. On the other hand, deep learning offers new tools in Bayesian modelling, e.g. to learn flexible nonparametric priors or computationally efficient posterior distribution approximations. State of the art The field of machine learning has recently been much impacted by … processing, to cite just a few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be accurately estimated. Bayesian statistics offers a theoretically well-grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At the same time, the theory More ❯

Are you a Data Scientist with a background as an Actuary? Do you want to work in a high-impact hub team that optimises modelling processes and applies Bayesian techniques to commercial insurance? We are looking for a skilled Data Scientist to join a centralised team, collaborating with Data Scientists, Python developers, Actuaries and senior insurance experts … to drive innovation in pricing and risk assessment by developing GLM, Gradient Boost, Bayesian and Linear Regression models for pricing models. What You’ll Be Doing: Enhancing actuarial models with advanced statistical and machine learning techniques. Developing and optimising pricing models using R, tidyverse, Python, and cloud-based tools like AWS and Snowflake. Working on a variety … of global commercial lines projects, with some exposure to personal lines. Collaborating with stakeholders across the business to improve modelling processes and decision-making. Supporting the integration of Bayesian models into the pricing framework. What We’re Looking For: Must have: Actuarial background with (ideally a fully or partially qualified Actuary) (eg – FIA, AIA, CERA, FSA etc More ❯

years of experience in predictive modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying data quality issues, and handling More ❯

years of experience in predictive modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying data quality issues, and handling More ❯

developed front-end componentry Interested in shaping the backend Python API or becoming involved in full stack development. Desired/Obtained Qualifications and experience: Some general knowledge of Bayesian networks or probability would be helpful Experience working closely with data scientists and other stakeholders, not just developers, and be comfortable demoing their work The product will be More ❯

of impactful research with publications in top journals/conferences. Experience leading research projects or mentoring junior scientists (for senior roles). Background in generative models (e.g., diffusion, Bayesian Flow Networks) or multimodal ML Interest or experience in biological domains like drug discovery, genomics, proteomics, protein design etc. Responsibilities: Drive cutting-edge research in multimodal generative models More ❯

and Duties Your day-to-day duties and responsibilities will include, but won’t be limited to building and improving sports prediction models To research recent advances in Bayesian updating to better extract signals from noisy data To design and build your own models using your own ideas To work closely with expert traders and developers to More ❯

forecasting, optimization, recommender systems, causal inference, or Bayesian methods. Proficiency in programming languages used in machine learning and familiarity with common frameworks. Solid grasp of statistical methods and software development best practices. Ability to work independently, manage timelines, and deliver prototypes or models aligned with business needs. Strong collaboration skills and comfort working across technical and More ❯

Machine Learning Scientist (General Interest) Date: Aug 1, 2024 We are looking for a Machine Learning Scientist in Bayesian Optimisation. You will be working closely with scientists in the materials space to build machine learning models (OptApps) that will inform laboratory experimentation schedules, anywhere from complete-manual to fully … self-driving. Responsibilities: Researching the right Bayesian Optimisation techniques for a variety of experimentation challenges: multi-fidelity, multi-source, multi-step (generally known as ‘grey-box’ methods). Implementing these models for partners in the pharmaceuticals industry, focusing on ease of usability and interpretation. Validating the effectiveness of the models and tackling deeper research challenges, with … the opportunity to publish. Minimum Requirements: You must have relevant experience in Machine Learning, specifically Bayesian Optimisation, at MSc/PhD level to complete the above work. Location: Hybrid/Oxford/London (anywhere in the UK) Matterhorn Studio is leading a paradigm shift towards peer-reviewed plug-in Bayesian Optimisation. We’re looking More ❯

experiment analysis techniques, including Bayesian and Causal Inference. Modeling Skills: Experience working on advanced modelling concepts. SQL and Statistical Analysis: Strong proficiency in SQL and statistical methods for analyzing complex data sets. Proven experience in conducting impact analysis to assess viable opportunities and scoping their relevance. Track Record: A successful track record of managing complex projects More ❯

you want to login/join with: The Quant team at Mustard Systems create predictive models that are globally recognised as best-in-class. We use cutting-edge statistical methods to model a wide variety of sports, often developing our own techniques. We're looking for talented individuals to join our team. You can expect: To research recent advances … in Bayesian updating to better extract signals from noisy data To join a close-knit, collaborative team of mathematicians and enjoy a high level of responsibility from an early stage To design and build your own models using your own ideas To work closely with expert traders and developers to improve your models Requirements What we're More ❯