26 to 50 of 207 Experimental Design Jobs in the UK

Science, Engineering, or related fields Programming skills in Python Proficient understanding of fundamental AI and ML techniques Practical experience with statistical data analysis and experimental design Curiosity, creativity, resourcefulness, and a collaborative spirit Effective verbal and written communication skills with technical and business audiences Demonstrated ability ...

machine learning.Proficiency in Python and modern data science libraries.Knowledge of deep generative modelling techniques.Experience working with LLM APIs or open-source LLM frameworks.Understanding of experimental design and model evaluation.Exposure to cloud platforms such as AWS, Azure or Google Cloud.Strong communication and collaboration skills.Desirable SkillsExperience within market research ...

Machine Learning Senior Associate in the Asset Management Data Science Team, you will build the technical foundation for intelligent investor tools. You will design context-management capabilities, develop LLM-powered applications, and create reliable pipelines for enterprise and investment data. You will partner with investment, data science, engineering … Master’s degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or a quantitative field Demonstrate a strong foundation in statistics, probability, experimental design, and machine learning, with sound judgment in method selection and interpretation Possess hands‐on experience building, deploying, and scaling LLM-powered applications ...

what matters: trust, ownership, and the freedom for people to do great work without unnecessary layers or bureaucracy. We're fully remote by design, genuinely global, and united by a shared mission to make travel simpler for everyone. Your Next Destination Location: Remote. Contract: Full-time, permanent. Benefits … measurement (attribution, incrementality or marketing mix modelling), with a track record of analysis that changed real budget decisions. Solid grounding in causal inference and experimental design: randomised and quasi-experimental methods such as synthetic control and difference-in-differences, simulation-based power analysis to size ...

building expertise in responsible AI and predictive science within a highly collaborative, international environment. What you will do Develop predictive models using historical and experimental data to support decisions across Crop Protection R&D. Prepare, integrate and analyse heterogeneous datasets from internal and external sources, ensuring data quality … years of relevant professional or research experience; internships, academic projects and thesis work may be considered. Strong foundation in machine learning, statistical modelling, experimental design and model validation. Proficiency in Python for data science and machine learning, including libraries such as 'pandas', 'scikit-learn', 'PyTorch' or 'TensorFlow ...

shaping the future of cybersecurity as you lead our efforts against next-generation AI threats. You will have the opportunity to design impactful tools, collaborate with experts, and influence strategic decisions that safeguard our global operations. Your expertise will help us stay ahead of evolving adversaries and foster … agents, or advanced machine-learning architectures Proficiency in Python, SQL, and data visualisation tools Background in cybersecurity research or related technical fields Experience in experimental design, causal inference, statistical modelling, and A/B testing Strong software engineering skills, preferably in Python Ability to communicate complex concepts ...

Foundational knowledge of AI, machine learning, and statistical modelling. Programming proficiency in Python, with familiarity in ML frameworks and scientific computing tools. Understanding of experimental design, model evaluation, and reproducibility in research.Strong analytical and problem-solving skills. Clear written and verbal communication skills appropriate for scientific collaboration. ...

learning and deep learning methods Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals Experience with big data ...

Monitor models in production, identify performance drift, propose improvements, and support ongoing recalibration or retraining where required. Apply probability and statistical inference to design experiments, interpret results, and provide clear recommendations to stakeholders. Contribute to high quality data practices by identifying data quality issues, supporting cleaning and normalisation ...

decisions are made and reviewed. Provide the guidance, tooling and protocols that improve your partner teams' experimentation velocity without sacrificing rigour. Lead the design of experiments where a standard A/B test is not sufficient, including low-traffic surfaces, users competing for the same supply, long‐horizon … with a proven track record of supporting and influencing a product 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 ...

of Computer Science and Technology (CST) at the University of Cambridge. The goal of this PhD programme is to launch one "deceptive by design" project that combines the perspectives of human-computer interaction (HCI) and critical computing. Topics of interest include: Persuasion and deception in conversational agents across … skills, including the ability to work with people of diverse backgrounds and across disciplinary conventions Solid understanding of research methods involving human participants, especially experimental design Basic understanding of data analysis approaches and tools, including statistics platforms like R and/or methods like thematic analysis Familiarity ...

deep learning methods Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumP da, Scikit-Learn, Pandas) Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals Experience with big data ...

Practice Group/Department Legal Innovation, Design & Technology - London Job Description Norton Rose Fulbright is a global law firm with more than 3,000 lawyers advising clients across locations in the United States, Europe, Canada, Latin America, Asia, Australia, Africa and the Middle East. We provide a full … comparable tools. Experience with search indexes, vector databases, graph databases, knowledge graphs, ontologies or master-data-management techniques. Experience with MLOps, model monitoring, experimental design, causal inference, forecasting, recommendation systems or optimisation. Experience with legal, financial, client, market or professional-services data, especially licensed external data ...

KPIs and suggest and experiment changes that can be made on site and to commercial propositions to drive growth. You’ll lead experimentation design and analysis, delivering insight that directly shapes roadmaps and priorities. You’ll partner closely with Product Managers, UX and cross-functional teams to understand … Strong analytical mindset with the ability to structure ambiguous problems and translate data into actionable insight Solid grounding in statistics, including hypothesis testing and experimental design, with the ability to apply these in real-world product environments Advanced SQL skills and strong experience with Python (pandas, NumPy ...

/ML techniquesand a strong grasp of current state of the art in specific areas of expertise Practical experience with statistical data analysis, experimental design and creation of meaningful benchmarks and metrics for evaluation. Effective verbal and written communication skills with the ability to address both technical ...

onboarding operations and software engineering Builds robust, scalable, and reusable Artificial Intelligence and Machine Learning capabilities Works with software engineering to design and deploy services that can be integrated with strategic systems and processes Learns about and understands our supported business to drive practical and successful solutions Conducts … Machine Learning or related field Proficient understanding of fundamental Artificial Intelligence and Machine Learning techniques Practical experience with statistical data analysis and experimental design Programming skills in Python Effective verbal and written communication skills with technical and business audiences Demonstrated ability to work on multi-disciplinary teams ...

Statistical Analysis & DoE: Apply Design of Experiments (DoE) and Statistical Process Control (SPC) methodologies to evaluate process windows, improve repeatability, and analyse experimental data using statistical tools (e.g., Minitab, Python). Yield & Defect Troubleshooting: Participate in structured root-cause investigations (such as 5-Why, 8D, and FMEA … Tools: Proficiency in operating SEM, surface profilometers, ellipsometers, and optical inspection tools for thin-film and pattern analysis. Data & Problem-Solving: Solid understanding of experimental design principles (DoE) and data analysis techniques. Communication: Clear written and verbal communication skills with the ability to document technical procedures ...

model is scientifically ready to move forward: review validation, robustness across batches, sites and instruments, and the risk of overfitting. Spectral interpretation and experimental design (about 40%) Interpret raw optical signatures from plasma and nanoparticle samples; diagnose artefacts such as baseline drift, autofluorescence, scattering and instrument variability. … information-rich and scarce clinical samples are used well. Mentor scientists and data scientists on the physics of the data and on rigorous experimental practice. External scientific partners (about 20%) Lead the scientific relationship with CROs, contractors and academic groups: write technical statements of work, set data and code ...

with backend engineers on the infrastructure the harness runs on, and with content engineers who use it every day. What you'll do Design and build the creative harness end to end: planning, tool use, context, memory and multi-step execution, so agents can run long, open-ended … tools, planning across steps and acting on the results. A chat interface over retrieval is not enough on its own. Strong foundation in statistics, experimental design and research: you can tell a statistically significant improvement from noise. Designed and shipped evals for outputs that ...

Generalisation , will investigate how the composition, diversity and annotation of biological training datasets influence model performance and generalisation. The successful candidate will design and undertake controlled computational experiments to identify which dataset properties contribute to model capabilities and distinguish genuine generalisation from memorisation, data leakage and similarities between … programming skills and practical experience of training, adapting or evaluating machine-learning models. You will also have an understanding of the main computational and experimental techniques used in generative or engineering biology. You will demonstrate strong quantitative and analytical skills, including experimental design, statistical analysis ...

integration, technology development, product engineering, validation, and applications teams, you will translate technical questions into actionable characterization programs and generate clear recommendations based on experimental evidence. In addition to project leadership responsibilities, you will contribute technical expertise in optical and electro-optical characterization, data analysis and interpretation, supporting … complex technical work packages or projects. Strong data analysis and problem-solving skills with attention to measurement quality and scientific rigor. Demonstrable experience in experimental design, validation and interpretation of results. Experience with programming and data analysis tools such as Python, MATLAB or equivalent. Knowledge of automated ...

coding assistants as the default mode of building; direct, validate, and course-correct AI-generated code at engineering speed. Go beyond vibe coding: design and build agentic workflows that automate delivery end-to-end, and run delivery bot meshes as part of day-to-day engineering, guiding … engineering, or evaluation.Experience designing, training, evaluating, or tuning machine learning or deep-learning models using frameworks such as PyTorch or TensorFlow.Experience with statistical analysis, experimental design, or building data pipelines using platforms such as Databricks or Apache Spark.Experience using AI-assisted software development tools, such as Claude ...

gain hands-on experience working alongside senior researchers and industry experts in a collaborative environment. You will be involved in data collection, algorithm development, experimental design, and analysis, contributing directly to research outputs that have real-world applications. This role is ideal for fresh graduates ...

written communication skills. Strong machine learning and statistical background. Write code and review others' in a shared codebase in Python. Practical SQL knowledge. Design experiments and collect data. Experience with distributed analytics and data tooling such as Spark and Databricks. This role has on-call shifts, as part ...

processors through advanced control theory, pulse optimisation and rigorous processor characterisation. You’ll develop and validate techniques that improve gate fidelity and stability, turning experimental breakthroughs into scalable calibration capabilities while providing insight that shapes future chip design. The Role As a Quantum Engineer specialising in control … experimentally validate high-fidelity single- and two-qubit operations. Design, optimise and benchmark pulse shapes using quantum control, numerical optimisation and experimental techniques. Characterise gate error mechanisms, including coherent error, leakage, crosstalk, drift and control-chain distortion. Build robust experimental pipelines, data-analysis workflows and performance ...