76 to 100 of 187 Experimental Design Jobs in England

language. You will turn promising research into practical experiments and product improvements. Responsibilities Research multimodal approaches for speech, audio, video and behavioural analysis Design experiments, evaluation methods and calibration studies Prototype new scoring and coaching techniques Work with engineering teams to move successful ideas toward production Document findings … Python and experience with modern ML research tools Experience in at least one of computer vision, speech/audio analysis or multimodal learning Sound experimental design, statistics and model-evaluation skills Ability to balance research quality with practical product needs Nice to have Advanced degree or equivalent ...

days remote Salary: £76,000 pa What you'll be doing: Analyse marketing performance, market trends and growth opportunities to inform strategic decisions. Design and evaluate experiments to measure marketing effectiveness and incremental growth. Build dashboards, develop performance metrics and translate data into clear, actionable recommendations. Deliver compelling … Strong background in Marketing Analytics, Marketing Science or Growth Analytics , ideally within big tech, digital platforms or leading agencies. Proven experience in statistical analysis, experimental design and marketing measurement . Strong dashboarding, data interpretation and analytical problem-solving skills. Excellent data storytelling and presentation skills , with ...

Foundation funded project ‘Air Quality Sensor Networks’. This project is likely to involve hierarchical Bayesian models, nonparametric Bayesian inference, graphical models, active learning, experimental design, and issues in spatiotemporal inference such as non-stationarity and non-separability. Requirements include a PhD or equivalent qualification in Statistics ...

Foundation funded project ‘Air Quality Sensor Networks’. This project is likely to involve hierarchical Bayesian models, nonparametric Bayesian inference, graphical models, active learning, experimental design and issues in spatio-temporal inference such as non-stationarity and non-separability. The expectation is that you will produce breakthrough ...

Skills Research experience or experience working in a commercial AI research environment Strong understanding of AI fundamentals and practical experience with data analysis and experimental design Experience deploying AI/ML applications in a production environment Familiarity with distributed computing patterns for training, serving, and persistence of ...

commercial AI research labs, in a Director level or equivalent role Deep understanding of AI fundamentals and practical experience with data analysis and experimental design Proventrack recordof deploying AI/ML applications in a production environmentat scale Familiarity with distributed computing patterns for training, serving, persistence of ...

Develop and maintain process documentation, standard operating procedures and best practices. Quality & Continuous Improvement Drive process stability, yield improvement and operational efficiency. Apply Design of Experiments (DoE), Statistical Process Control (SPC), Measurement System Analysis (MSA) and Gauge R&R. Lead structured root cause investigations using methodologies such ...

developing novel algorithms at the intersection of computer vision, deep learning, metric learning, 3D geometry, and vision‐language modelling. You will formulate problems, design experiments, and drive your work from prototype through to production‐grade systems that power real‐time spatial intelligence across complex environments. ...

developing novel algorithms at the intersection of computer vision, deep learning, metric learning, 3D geometry, and vision-language modelling. You will formulate problems, design experiments, and drive your work from prototype through to production-grade systems that power real-time spatial intelligence across complex environments. ...

early case for them, working with the relevant teams on IP, data rights and commercial considerations. Essential Knowledge, Skills, and Experience A background in experimental science, ideally life sciences (including, but not limited to, molecular & cellular biology, microbiology, plant biology), with first‐hand knowledge of how lab work actually … problem framing through to adoption by real users, in an R&D, product or consulting setting. Structured problem solving and strong quantitative reasoning, including experimental design and working statistics. Effective communication with academic scientists, engineers and executives alike, including the ability to disagree constructively with each ...

Data team. Our Data Engineers in Autonomous Systems work within a wider project, AutoLabs. This is where EIT automates the scientific discovery loop: experimental design, execution, analysis, and the learning that feeds back into the next experiment and the lab itself. The AutoLabs data platform captures, stores … scientists to build the data layer that makes our autonomous operations possible. You will contribute to an engineering culture that values robust system design, testing, and deep collaboration, but allows flexibility for rapid prototyping and responsiveness to changing landscapes. Day-to-Day, You Might: Build high-throughput ...

combine machine learning, recommender systems and Generative AI to solve real-world customer problems while delivering measurable commercial impact. The Opportunity You’ll design, build and deploy production-grade machine learning models that power highly personalised customer experiences. From recommendation algorithms and retrieval systems through to LLM-powered … experimentation, high-quality code and scalable machine learning systems. Skills & Experience Strong commercial experience with Python Excellent understanding of machine learning, statistical modelling and experimental design Experience developing and deploying end-to-end machine learning products into production Knowledge of recommender systems, personalisation, retrieval or ranking techniques ...

data and AI are built into how science gets done. Benchling is the AI platform for biotech R&D. Scientists use Benchling to design experiments, capture structured data, and run AI agents and models directly in their workflows. Over 200,000 scientists around the world trust Benchling ...

analyse and evaluate agentic systems rigorously enough to say where and why they go wrong, and we use what we learn to design better ones. We are not simply building a better harness; the best harness will change with every new model, task and generation of silicon. … Evaluations or benchmarks you 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 ...

experience in one or more of: quantitative systems performance analysis, statistical modelling, simulation validation, uncertainty quantification, scientific/HPC software or the theory of experimental design. - The ability to collaborate with specialists and explain both a result and the limits of the evidence supporting it. - Practical experience using … coding agents and checking their output. Experience with model discrepancy, sensitivity analysis, Bayesian methods, surrogate models or sequential experimental design would be useful. So would familiarity with computer architecture or inference systems. We welcome complementary backgrounds: strong systems engineers developing uncertainty expertise, and engineers with modelling ...

data and AI are built into how science gets done. Benchling is the AI platform for biotech R&D. Scientists use Benchling to design experiments, capture structured data, and run AI agents and models directly in their workflows. Over 200,000 scientists around the world trust Benchling ...

bring statistical rigour to decisions we'd otherwise make by feel. You size opportunities, design experiments, build models and ship insight that changes what we build next. What you'll build Models that quantify risk, propensity, quality and forecast key business outcomes. Experimentation frameworks used across the group. ...

five key areas: (1) Statistical & Mathematical Foundations, (2) Algorithmic Innovation & Implementation, (3) Data-Driven & Scalable Systems, (4) Hardware & Systems Understanding, and (5) Rigorous Experimental Design. Below is a synthesis of how each paper highlights these qualities, with references illustrating why they matter for building robust, impactful deep learning models. … work show how exploiting specialized matrix decomposition, custom memory hierarchies, or quantization approaches can lead to breakthroughs in speed or energy efficiency. 5. Rigorous Experimental Design Controlled Comparisons & Ablations Nearly all papers—Whisper, FSQ, Mamba-2, BASE TTS—use systematic ablations to isolate the impact of individual ...

legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks … evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable ...

customer teams toselect research priorities with measurable production impact. Share results throughinternal reports, technical blogs, papers, and open-sourceartifacts, and mentorengineers and scientists on experimental design, scientific rigor, and model/systemtrade-offs. Must-haves APhD in computer science, machine learning, ML systems, computer systems, computer architecture … ability to turn ideas into experiments and working prototypes. Deepknowledgeof LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-servingtrade-offs. Strong experimental designskills coveringablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publicationsat venues such asNeurIPS, ICML ...

decisions. With 5+ years of data science experience and strong communication skills, you will partner with Product and Growth teams to frame problems, design experiments, and deliver data-driven solutions #J-18808-Ljbffr ...

world plants, alongside process and deployment engineers. Flexible working is offered, and equity aligns you with the company’s future success. You’ll design experiments, push the boundaries of learning under uncertainty, and translate #J-18808-Ljbffr ...

of fibre handling techniques including stripping, cleaving, fusion splicing, polishing, and inspection. Experience supporting customers, manufacturing partners, or product development projects. Knowledge of Design of Experiments (DOE), product testing, and validation processes. Degree, HNC/HND, apprenticeship, or equivalent qualification in Engineering, Physics, Electronics, Photonics, or a related ...

ladder and provide key inputs into company roadmap activities. What You’ll Do: Lead complex scientific research projects from initial concept and experimental design through to implementation, analysis, reporting and technical recommendations. Work with key partners to develop quantum technologies into novel timing applications. What You Need ...

program within our hybrid work model. Job responsibilities Perform exploratory data analysis, feature engineering, model development, and evaluation using structured and unstructured datasets Design and build prototypes of agentic AI systems, including multi-step workflows that use tools and reason across tasks Apply and experiment with LLM techniques … such as retrieval-augmented generation, tool and function calling, fine-tuning, and agent harness design Develop evaluation frameworks to measure the quality, reliability, and safety of LLM and agent outputs Collaborate with mentors, business stakeholders, and engineers to turn business problems into AI solutions and help move them ...