AI for Science Summit 2026

⌖ Cambridge · Churchill College, University of Cambridge

Hosted by ICAIDM

ICAIDM 2026 brings researchers and practitioners to Cambridge for three days of technical presentations, keynotes and discussion spanning artificial intelligence, digital infrastructure and data-driven management. The in-person conference takes place from 15–17 October 2026 at Churchill College, University of Cambridge, in Cambridge. Research themes The programme and call for papers cover both foundational AI research and real-world applications. Areas of interest include: - Machine learning and AI foundations: deep learning, reinforcement learning, transfer and meta learning, federated learning, model optimisation, explainable and trustworthy AI, and AI for scientific discovery - Communication and networking: intelligent wireless networks, traffic prediction, semantic communications, edge intelligence, AI-driven network management, and network security - Data and digital technologies: data engineering, big data analytics, knowledge graphs, cloud and edge computing, distributed platforms, IoT, cyber-physical systems, and data privacy - Digital management and AI applications: digital transformation, intelligent decision support, business analytics, smart cities, finance, industrial systems, operations optimisation, and emerging AI applications Attendees can expect an academic conference format built around paper presentations and keynote sessions. Its broad scope creates opportunities to compare methods across computer science, communications, infrastructure and management rather than treating AI as an isolated discipline. It is best suited to researchers, academics and technically focused industry professionals working on AI systems or digital management, as well as authors looking to present original work to an international audience. Publication and submission details Accepted invited and contributed papers are scheduled for publication in the ACM International Conference Proceeding Series (ACM ICPS) and submission to EI Compendex and Scopus for indexing. Proceedings papers must be at least eight pages. Full papers should be supplied in both Word and PDF formats through the conference submission route. Key deadlines are:… Register for this event

⌖ Cambridge · West Hub (Room D)

Hosted by C2D3

Explore how AI and data science move from academic research into real-world organisations with Google partnerships leader Christina Matteotti in Cambridge. Taking place on Thursday 22 October 2026, from 4:00–5:00pm, this in-person Beyond Academia seminar offers University of Cambridge researchers and students a practical window into the opportunities and challenges surrounding applied AI. The session will be held in Room D at the West Hub, JJ Thomson Avenue, Cambridge. From research to real-world deployment Part of the University of Cambridge’s Beyond Academia: AI & Data Science Applications seminar series, the talk is designed to bridge the gap between high-level research and implementation inside organisations. It will examine the transformative role of data and artificial intelligence beyond the university environment, while helping attendees better understand non-academic career pathways. The seminar series explores areas including: - The transition from academic AI and data science research to operational use - Technical and architectural challenges involved in implementing AI innovation - Organisational and operational considerations around real-world deployment - Opportunities for researchers applying their expertise outside academia - Career perspectives from leaders and professionals working beyond universities Speaker and format The invited speaker is Christina Matteotti, Head of Universities, EMEA Partnerships. The event is structured as an engaging, one-hour in-person talk rather than a hands-on workshop. Attendees will have plenty of opportunity to ask questions, making it a useful setting for exploring both practical implementation issues and possible career directions. The seminar is organised by C2D3, the Cambridge Centre for Data-Driven Discovery. Everyone at the University of Cambridge is welcome, but the content is particularly aimed at PhD students, postgraduate researchers and Early Career Researchers. It may also be valuable for students interested in AI, data science, innovation partnerships or translating specialist research into impact within organisations.… Register for this event

⌖ Cambridge · The Glasshouse @InnovateCambridge

Hosted by Cambridge Centre for Data-Driven Discovery

Explore how the Financial Times turns data and AI strategy into organisational value with Chief Data Officer Kate Sargent at this University of Cambridge seminar. From research to real-world AI Taking place in person on Tuesday 27 October 2026, from 4:00pm to 5:00pm, this Beyond Academia seminar offers a practical perspective on deploying data science and artificial intelligence beyond the university environment. The session will be held at The Glasshouse @InnovateCambridge, 100 Hills Road, Cambridge, CB2 1LQ. The Beyond Academia: AI & Data Science Applications series is designed to bridge the gap between high-level research and implementation inside organisations. Invited leaders and professionals examine the technical, architectural and operational realities of AI innovation while introducing attendees to career opportunities outside academia. Hear from Kate Sargent Kate Sargent, Chief Data Officer at the Financial Times, is responsible for the organisation’s Data and AI Strategy and for creating value from its data assets. Since joining the FT in 2023, she has led its award-winning Data & AI Governance, Data Science & AI, Analytics, Research and Business Intelligence disciplines. Kate brings 30 years of experience across major consumer-facing organisations in retail, travel and media. Her work focuses on using data to understand and influence customer behaviour and generate commercial value. Alongside her executive role, she is an industry commentator, board adviser and mentor, with a particular commitment to early-years skills development and increasing awareness of careers in data and analytics. Attendees can expect insight into: - How data and AI strategy operates within a global media organisation - The challenges and opportunities involved in implementing AI innovation - The role of governance, analytics, research and business intelligence - Translating data assets into practical and commercial value - Non-academic career pathways in data science and AI The format is an engaging one-hour talk with plenty of opportunity for audience Q&A, giving researchers and students direct access to an experienced data leader. Who should attend?… Register for this event

Sponsors: Bennett School of Public Policy · C2D3

⌖ Cambridge · West Hub, Room E, University of Cambridge

Explore how cutting-edge AI research becomes scalable, real-world products with BBC Head of Applied AI Dr Nikolay Burlutskiy in Cambridge on 10 November 2026. This in-person Beyond Academia: AI & Data Science Applications seminar offers a practitioner’s perspective on moving artificial intelligence beyond research and into complex organisations. Hosted by the Cambridge Centre for Data-Driven Discovery (C2D3), it takes place from 4:00–5:00pm on Tuesday 10 November 2026 in Room E at the University of Cambridge’s West Hub, JJ Thomson Avenue, Cambridge. From AI research to deployment The Beyond Academia series bridges the gap between high-level academic research and real-world implementation. Its invited talks examine the technical, architectural and operational challenges involved in deploying AI innovation, while also highlighting career paths outside academia. The format is an engaging one-hour talk with plenty of opportunity to put questions directly to the speaker. Attendees will hear from Dr Nikolay Burlutskiy, Head of Applied AI at the BBC, where he focuses on translating advanced AI research into scalable products used by millions. His career spans more than 15 years across AI research, technical product development and international enterprise strategy. Burlutskiy’s experience includes: - Serving as Senior Engineering Manager at Mars and leading the architecture and deployment of the Mars AI Experiences platform, an enterprise generative AI capability supporting more than 170,000 associates. - Working as Director of ML & AI at AstraZeneca, where he led scientists and engineers applying generative AI and computer vision to drug discovery, clinical data harmonisation and histopathology automation. - Leading AI technical product development at Sweden’s ContextVision and contributing to the creation of spin-out Inify. - Beginning his industry career as an AI engineer at Samsung Electronics in South Korea. He holds a PhD in AI, has more than 35 peer-reviewed publications and US patents, and contributes to the wider AI community as a speaker, conference organiser and reviewer. Who should attend?… Register for this event

◎ Online

Hosted by Massachusetts Institute of Technology (MIT)

Build production-ready AI and machine learning solutions without writing a single line of code. This comprehensive 12-week online program from MIT Professional Education transforms aspiring data scientists and business professionals into AI practitioners through hands-on, no-code workflows. Starting November 27, 2026, you'll master the latest AI technologies—from supervised learning and neural networks to Generative AI and autonomous agents—using intuitive platforms that eliminate traditional coding barriers. What You'll Master This industry-aligned curriculum covers the complete AI development lifecycle across 10 core modules and additional self-paced content. You'll explore data science fundamentals, prediction methods using regression and neural networks, decision systems, recommendation engines, and computer vision techniques. Advanced modules dive deep into Generative AI foundations, prompt engineering, and Agentic AI—the cutting edge of autonomous intelligent systems. The program culminates with critical training in ethical and responsible AI deployment. You'll work with powerful no-code platforms including KNIME, RapidMiner, Teachable Machine, ChatGPT, Gemini, NotebookLM, and Dall E. These tools enable rapid prototyping, testing, and operationalization of machine learning models without traditional programming knowledge. Hands-On Learning Theory meets practice through 3 comprehensive projects and over 20 real-world case studies spanning hospitality, marketing and advertising, EdTech, healthcare, food and nutrition tech, and e-commerce. You'll transform raw data into actionable business insights, build classification systems, deploy neural network solutions, and create recommendation engines—all using no-code methodologies that reflect current industry practices. World-Class MIT Faculty Learn directly from MIT's leading researchers and educators, including Stefanie Jegelka, Caroline Uhler, John N. Tsitsiklis, Munther Dahleh, and Devavrat Shah. Their expertise ensures you're learning cutting-edge techniques aligned with academic research and industry application. Flexible Online Format This fully online program requires 6-12 hours weekly commitment over 12 weeks (approximately 80 total study hours). The asynchronous format allows you to balance learning with professional and personal commitments while progressing through structured modules. Upon successful completion, you'll receive a Certificat

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⌖ Cambridge · Madingley Hall Conference Centre

Hosted by Flivo

One-day summit at Madingley Hall Conference Centre, Cambridge on 2026-11-29 exploring how intelligent AI agents are redefining decision-making.