IEEE Signal Processing Society Invites AI and Edge Computing Course Proposals for 2026

Author – Ritesh Ranjan: The IEEE Signal Processing Society (IEEE SPS) has invited industry experts, researchers, engineers and technology professionals to submit proposals for short, practical courses as part of its 2026 Educational Series. The initiative aims to provide industry-focused professional development opportunities in emerging areas such as artificial intelligence, edge computing, signal processing, trustworthy AI and physics-informed machine learning.
Experts interested in contributing to the programme can submit proposals for approximately three-hour courses, with the deadline for submissions set for 30 August 2026.

Selected courses will be delivered during 2026 and made available through the IEEE Learning Network, allowing instructors to reach engineers, professionals and technology decision-makers across the world.
The initiative provides subject-matter experts with an opportunity to share practical knowledge while IEEE manages the production and delivery process.
IEEE SPS Focuses on Practical Industry Learning
The IEEE Signal Processing Society’s Educational Board is looking for courses designed specifically for working professionals.
Rather than focusing primarily on theoretical concepts, proposed courses are expected to provide practical knowledge that participants can apply in industrial environments.

The course duration should be approximately three hours, making the programme suitable for professionals who want to gain specialised skills without committing to longer academic programmes.
Under the programme, instructors will develop and provide the course content, while IEEE will take responsibility for the production process and distribution through its learning platform.
The 2026 Educational Series forms part of the broader professional development activities conducted by IEEE SPS, which provides learning opportunities for both IEEE members and non-members.

Key Areas for IEEE SPS Course Proposals
The IEEE Signal Processing Society has highlighted several emerging technology areas where professional training is becoming increasingly important.
However, experts are also encouraged to propose courses covering other relevant topics related to signal processing and emerging technologies.
1. Foundation Models in Industry
Foundation models are rapidly becoming an important part of enterprise AI applications.
This area may include courses focused on multimodal large language models capable of processing multiple types of information, including text, images, audio and video.

Industry-focused courses could explore how foundation models can operate as an intelligence layer for robots, autonomous systems and physical AI agents.
Another potential area is the integration of foundation models with continuous signal streams generated by cameras, microphones and industrial sensors.
Courses may also examine vision-language-action models, which combine visual understanding, language processing and physical actions.
Such systems could become increasingly important in robotics, manufacturing, autonomous machines and intelligent industrial systems.
2. Edge AI and Efficient Signal Processing
Edge computing is another major focus of the IEEE SPS educational initiative.
Many organisations are moving AI processing away from centralised cloud infrastructure and towards devices located closer to where data is generated.
These devices may include smartphones, industrial sensors, embedded systems, robots and Internet of Things devices.
Running complex AI models on such hardware can be challenging because these devices often have limited computing power, memory and energy resources.
Courses proposed under this category may address technologies such as:
- Extreme model quantisation
- Hardware-aware AI optimisation
- Efficient neural-network architectures
- Neuromorphic engineering
- Embedded signal processing
- On-device machine learning inference
These techniques can help organisations deploy AI applications on relatively low-cost hardware while reducing latency, cloud dependency and operational expenses.
Practical knowledge in Edge AI is becoming especially important for industries including automotive engineering, manufacturing, healthcare technology, telecommunications and robotics.
3. Trustworthy AI and Signal Forensics
As AI-generated content becomes more sophisticated, organisations are facing new security challenges.
The IEEE SPS is therefore encouraging courses focused on trustworthy AI and signal forensics.
Potential subjects include deepfake detection, digital watermarking, adversarial attacks and secure sensor systems.
Deepfake technology, for example, can generate highly realistic artificial audio and video content. This creates challenges for organisations that need to determine whether digital media is authentic.
Signal watermarking can provide one method of identifying or protecting digital content.
Courses may also examine adversarial attacks in which sensor data or AI inputs are manipulated to produce incorrect system behaviour.
These security challenges are particularly relevant for autonomous vehicles, intelligent surveillance systems, robotics and other technologies that rely heavily on sensor data.
Industry professionals working in cybersecurity, AI governance and intelligent systems may therefore benefit significantly from practical training in this area.
4. Physics-Informed Machine Learning
Another important area highlighted by IEEE SPS is physics-informed machine learning.
Traditional machine-learning systems often depend heavily on large datasets. However, engineers and researchers may already have valuable knowledge about the physical systems generating those datasets.
Physics-informed machine learning attempts to incorporate scientific knowledge, engineering principles and signal-processing expertise directly into machine-learning models.
For example, engineers may use known physical equations or signal characteristics to guide model training.
This approach can potentially improve data efficiency, explainability and model performance.
It may also help machine-learning systems operate more effectively when they encounter conditions that were not well represented in their training data.
The topic is particularly relevant in areas including industrial engineering, communications, energy systems, medical imaging and scientific computing.
IEEE Learning Network Offers Global Professional Education
Courses selected for the IEEE SPS Educational Series will be distributed through the IEEE Learning Network.
The platform provides a wide range of engineering and technology learning resources designed for professionals at different stages of their careers.
IEEE Learning Network programmes cover areas such as artificial intelligence, communications, computing, electronics and engineering.
Some programmes may also provide professional development benefits such as certificates, Professional Development Hours (PDHs) and Continuing Education Units (CEUs).
By including selected SPS courses on the platform, instructors can potentially reach professionals working across industries and geographical locations.
Opportunity for Researchers and Industry Experts
The IEEE SPS call for proposals provides an opportunity for experts to contribute directly to professional education in rapidly developing technology areas.
Researchers can translate their technical knowledge into practical courses, while industry professionals can share lessons gained through real-world deployments.
Technology leaders may also use the platform to educate professionals about emerging engineering approaches and implementation challenges.
Potential instructors could come from sectors including artificial intelligence, robotics, cybersecurity, telecommunications, semiconductors, embedded systems and digital signal processing.
A strong course proposal should therefore demonstrate not only technical expertise but also clear practical value for working professionals.
How to Submit an IEEE SPS Course Proposal
Experts interested in participating in the programme should prepare a proposal for a course of approximately three hours.
The proposal should focus on practical learning and demonstrate how participants could apply the knowledge in real industry environments.
According to the announcement, the deadline to submit proposals is 30 August 2026.
Interested instructors can submit their proposals through the official Google Forms link provided by the IEEE Signal Processing Society.
Selected courses will become part of the 2026 Educational Series.
Why the IEEE SPS Initiative Matters
Artificial intelligence and signal-processing technologies are developing quickly, creating a growing need for continuous professional education.
Technologies such as foundation models, Edge AI, multimodal systems and intelligent robotics are moving from research environments into real-world industrial applications.
At the same time, organisations must address challenges involving security, computational efficiency, transparency and responsible AI deployment.
Short, specialised courses can help professionals understand these developments without requiring them to undertake lengthy academic programmes.
The IEEE SPS Educational Series therefore provides both instructors and learners with a platform for exchanging practical knowledge about technologies that could shape the future of engineering and industry.
For experts with hands-on experience in AI, signal processing, robotics, embedded technologies and cybersecurity, the 2026 call for course proposals presents an opportunity to contribute to the global professional-learning ecosystem.
Frequently Asked Questions
1. What is the IEEE SPS 2026 Educational Series?
The IEEE SPS 2026 Educational Series is a professional development initiative organised by the IEEE Signal Processing Society. It will feature short, industry-oriented courses covering emerging technologies and practical applications of signal processing.
2. Who can submit an IEEE SPS course proposal?
Researchers, engineers, industry experts, technology professionals and subject-matter specialists with practical expertise in relevant areas can submit course proposals.
3. What should be the duration of the proposed course?
Proposed courses should be approximately three hours long and should primarily focus on practical learning and real-world applications.
4. What topics is IEEE SPS looking for?
Priority areas include foundation models in industry, Edge AI, efficient signal processing, trustworthy AI, signal forensics and physics-informed machine learning. Experts may also propose other relevant subjects.
5. What is the last date to submit an IEEE SPS course proposal?
The deadline for submitting proposals for the 2026 IEEE SPS Educational Series is 30 August 2026.





