51 to 75 of 77 Bayesian Methods Jobs

built that other people went on to use Broad training in empirical method, potentially from a field outside machine learning – experimental design, Bayesian inference, model selection, significance testing, uncertainty quantification Experience with automated grading and model-based judging, and a rigorous account of its failure modes and ...

and validation of spectral data in high-dimensional, small-sample settings (p ≫ n, typically N < 200 per cohort). Choose and develop the right methods: chemometrics and dimensionality reduction (e. g. PLS, PCA, wavelets, regularised and Bayesian models), and, where the data support it, physics-informed … regulated product development. The opportunity Own the scientific core of a proprietary diagnostic platform, work directly with iLoF's leadership, and see your methods shape a clinical test. iLOF in a nutshell As personalized medicine becomes the norm, scientists, patients, and healthcare systems worldwide face massive barriers in providing ...

organisation. Statistical & Experimentation Expertise: Authoritative command of experimentation in all its forms, from experimental design and variance reduction to causal inference, bandits and Bayesian methods. You should be able to develop and validate methodology, not only apply it. Technical & Modelling Expertise: Expert level proficiency in Python and … and growth over time, and to bring that perspective into how experiments are designed and interpreted. Standardising at Scale: Experience standardising processes, frameworks or methods across multiple teams, including the use of AI and automation to make good practice the default. Cross‐Functional Partnership: Proven ability to build strong ...

models, machine learning models, and visualisations for experiments that allow us to better understand the therapeutic potential of our antibodies. Implement and develop novel methods for a variety of applied antibody molecule engineering and drug development problems, from target ID to supporting pre-clinical operations. Work on challenging statistical … problems using Bayesian modelling; and optimisation problems using Active Learning & Bayesian Optimisation. Write high quality code to maintain and enhance our technology. Enable a data-first ecosystem at LabGenius through your proficiency at processing complex datasets and extracting key insights. What we’re looking ...

velocity, and tackle complex scenarios where traditional A/B tests fall short. You will build robust metric systems, apply Bayesian methods and multi-arm bandits, and mentor junior data scientists while ensuring products scale safely across markets and time. #J-18808-Ljbffr ...

exploration, training, deployment, and monitoring. Non-linear methods. You can think in terms of the cost function and inductive biases of your models. Bayesian methods. You've used hierarchical models, MCMC, or Bayesian updating in real work. Time series modelling. You've modelled temporal ...

Role We are looking for a biostatistician with expertise in Bayesian modelling to work on probabilistic modelling of physiological data and medical risk. The role focuses on developing statistical frameworks that combine multiple data sources to estimate the probability of life-threatening deterioration and support triage prioritisation. … engineers, medical advisors, and the product team to design models that operate in real-world environments with noisy and incomplete data. Responsibilities Develop Bayesian statistical models to estimate physiological deterioration risk from multi-sensor data Build probabilistic frameworks integrating vital signs, haemodynamic indicators, motion data, and environmental ...

Build production-grade Python packages Follow and build project scaffolds (PowerShell), follow coding standards, and reproducible workflows Transform analyses into standardized currently best-known methods (cBKM) Process data and automate data flows; manage and optimize data structure & data bases for big data sets Interact with product and service teams … Bachelor or Master Degree: Statistics/Computer Science/Data Science//Industrial Engineering Expert knowledge of Python, statistical modelling (frequentist and Bayesian) and data visualization Intermediate knowledge of PowerShell, R and SQL AI expert knowledge Project management Comfortable with non-recurrent, high-complexity engineering analyses ...

data and audiences we build for. Comfortable with sport-driven modelling decisions. Machine Learning & Statistics: Solid grounding in machine learning, supervised and unsupervised methods, and classical statistical techniques. Comfortable working with probabilistic models, uncertainty estimation and Bayesian inference. Model Development: Understanding of the full model training … Engineering: Experience building and maintaining production data pipelines, or working with AWS services such as Lambda, EventBridge and DynamoDB. Simulation: Experience with Monte Carlo methods or probabilistic simulation. AI Integration: Comfortable using AI-assisted coding tools such as Claude Code or Cursor as part of your everyday workflow, and ...

lifecycle, from research and problem formulation through to experimentation, model development and deployment. Working closely with engineers and domain specialists, you will use statistical methods, causal reasoning and machine learning to understand complex systems, identify underlying relationships and develop solutions that can be applied in real-world environments. … with the opportunity to influence technical direction and contribute to innovative AI applications. Key Responsibilities Develop and apply machine learning, statistical and causal inference methods to complex real-world problems. Design experiments and develop methodologies to identify causal relationships, test hypotheses and understand system behaviour. Translate complex and ambiguous ...

DUTIES: Apply advanced quantitative and statistical methods to support business model development and product innovation. Develop and implement Bayesian models and stochastic simulations to generate insights and predictions, including analyses of user behavior and system performance. Manage projects end-to-end, including data extraction, cleaning, and … higher in Data Science, Computer Science, Systems Engineering, or a closely related quantitative field. Experience completing at least two (2) projects utilizing Bayesian inference, stochastic modeling, or a combination of both, gained in either academic or professional settings. Demonstrated experience developing high-quality functional or object-oriented ...

predictive models look like in a betting context and build models on your own. Support the team in maintaining the codebase. Research new methods for future applications. ABOUT YOU: Required: Master’s degree or equivalent/higher qualification in one of the following disciplines: Data Science, Statistics, Mathematics, Software … closely related field. Experienced in predictive modelling, with a deep understanding of data analysis and modelling techniques such as GLMs, Time-Series Analysis, Bayesian Statistics, MCMC, neural networks, etc. Very strong skills in R or python. Advanced coding and analytical skills. Strong communication skills, fluency in English. ...

order to improve Eligible to work in London. And any of these would be great, but aren’t essential: Experience in applying machine learning methods to real-world problems Familiarity with supervised ML Familiarity with Time Series Forecasting Familiarity with supervised LLM chat and workflow automation Familiarity with Computer … Vision Familiarity with Bayesian Learning Familiarity with Causal Inference Familiarity with Recommender Systems Familiarity with Graphs and GNNs Familiarity with Reinforcement Learning Familiarity with Apache Spark or other distributed processing frameworks A paid 6-week sabbatical leave after four years For everyone, everywhere. We’re people building ...

waiting to be allocated it Genuine attention to detail — much of this work involves noticing when a number is wrong Nice to have Bayesian modelling experience, particularly hierarchical models Experience with AWS or comparable cloud tooling Familiarity with automated QA tooling and test coverage practices Experience with … marketing, ecommerce, or advertising data Not required Experience with attribution methodology, MMM, incrementality testing, or Bayesian modelling is genuinely an advantage at this level — but it isn't a filter. Our stack takes time to learn regardless of what you arrive with, and we'd rather hire ...

algorithms that power Biostream’s physiological monitoring and alert systems. You will work closely with the biostatistics team to translate probabilistic and Bayesian models into deployable algorithms that operate on real-world sensor data. The role focuses on building robust machine learning systems capable of detecting physiological … constrained connectivity. Responsibilities Design and develop machine learning algorithms for physiological signal analysis and risk detection Work closely with the biostatistician to implement Bayesian models and probabilistic frameworks Develop models combining vital signs, motion data, and contextual signals Build pipelines for sensor data processing, feature extraction, and ...

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 ...

mathematical models. Build prototypes and demonstrators on Quantum Dice's simulators, software stack and probabilistic processing unit hardware. Apply optimisation, probabilistic inference and AI methods such as QUBO, Ising models and Bayesian networks. Benchmark against classical solvers, CPUs and GPUs on solution quality, runtime and scalability. ...

uncertainty quantification, experimental design, causal inference and Bayesian modeling. Just as essential is commercial instinct: the judgment to design rigorous, workable methods within real product goals and constraints, built through meaningful, hands-on experience shipping at startup speed. You will report to the CTO and work … product, and customer context. You need complete information before acting, rather than exercising judgment under uncertainty and evolving guidance. You'd rather follow established methods than create structure from ambiguity; defining and refining our evaluation process is core to the job. You're uncomfortable owning the quality bar and ...

project leads. Apply modern deep learning for molecular design (GNNs, generative models, property prediction) where it complements traditional CADD methods. Contribute to productionising CADD methods into a reproducible, API-first toolkit; partner with Infrastructure on cost-effective GPU/HPC workflows. Own the computational design strategy on assigned programmes … Working knowledge of modern deep learning for molecular design (GNNs, generative models, property prediction), and a clear sense of when these complement traditional CADD methods rather than replace them. (Senior level) A track record of owning the computational strategy on active programmes, and of technical leadership — mentoring and influencing ...

Research Scientist, you’ll: Help shape our research and development agenda, turning challenges from live plants into well-defined research problems. Develop and implement methods for modelling, identification, optimisation and control of complex physical systems. Design rigorous experiments, establish strong baselines and understand when and why a method works. … process engineers to incorporate plant physics, operating knowledge and constraints into models and algorithms. Work with machine learning and deployment engineers to evaluate promising methods and bring them into production. Follow deployed performance, investigate failures, and use what you learn to guide further research. Communicate your findings clearly and ...

data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale … business problems. Deep understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code ...

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 ...

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 ...

also offers clear scope for growth: the role-holder will be encouraged to engage directly in the group’s research, helping develop new computational methods and contributing to publications, with responsibilities that can expand as the Cambridge node grows. Establishing and managing reliable version-control infrastructure for the group … within the group’s codes, together with the research team, profiling and optimising their performance. Investigating, prototyping and implementing machine-learning and advanced statistical methods (e.g. improved sampling for Bayesian parameter inference, surrogate models/emulators, simulation-based inference) to accelerate the group’s modelling and ...

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 ...