Program Manager - EMEA Academic Network

Cadence Design Systems, Inc.
Bracknell
1 year ago
Applications closed

Related Jobs

View all jobs

Technical Program Manager - Machine Learning - New York

Technical Program Manager - Machine Learning - New York

Technical Program Manager - Machine Learning - New York

Sr. Project Manager/Program Manager - Digital Twin / AIOps (OSS)

Senior Manager, Data Science - eBay Live

Faculty Fellowship Programme - Data Science - May 2026

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

The Cadence Academic Network Team focuses on introducing Intelligent system Design Technologies to the Global Academic Ecosystem. We provide easy accesses to industry grade Computational Software technologies in the areas of EDA, IP, VLSI, Hardware, RF, System Simulation, Molecular Biology and more. The Cadence Academic Network focuses on enabling fundamental research, building specialized talent communities, supporting workforce development and promoting the Cadence brand with the academic population. We implement Computational Software proficiencies such as Machine Learning and Artificial Intelligence through designed programs, teaching, research, systems and tools. We are hiring a leader to support and develop our Academic network in EMEA.

In this varied role, you will be expected to:

Build Strong academic relationships, design programs and solutions to proliferate technology adoption at universities, promote the Cadence brand, support workforce development for Cadence and Cadence customers, and develop specialized research communities to advance collaborative research of our technology. A strong and collaborative presence in the academic space is key to identifying high-potential, diverse, technical talent, technical and business collaborations, and influencing curriculums at key academic institutions. In addition, your support will be required to design and implement teaching and research programs in EMEA universities, function as an Applications Engineer for professors and students, by providing training and support on Cadence technology in partnership with Cadence Education Services and Ecosystem partners. You will be viewed as a brand representative at university events, serving as a point of contact within Cadence for university engagements to ensure coordination of interactions, cohesion and consistency in engagement strategy and best practices. You will be expected to collaborate with the Cadence internal client groups by delivering support and guidance on messaging, programs and partnerships to Cadence EMEA R&D and WFO, enabling and motivating them to act and represent Cadence in their location or discipline.

Job Qualifications

Graduate degree in CS or EE, with a passion for technology and education Application Engineering experience in the EDA and/or IP space Familiarity with a university setting with an understanding of the academic stakeholder and university processes. Academic connections – a plus. Customer-oriented with a high degree of personal initiative, hands-on approach and flexibility Project Management/Organizational Skills Team-oriented and works well across a variety of personalities and disciplines Excellent verbal, written and presentation communication skills

We’re doing work that matters. Help us solve what others can’t.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.