Robotics is becoming an increasingly important area of artificial intelligence and scientific research, creating new opportunities for engineers, researchers, and technical builders interested in solving complex real-world problems.
One fellowship that has attracted attention in this space is the Encode AI for Science Fellowship, backed by Pillar VC and powered by the UK’s Advanced Research + Invention Agency (ARIA). The program gives AI and machine learning researchers the opportunity to spend up to 12 months developing ambitious projects at the intersection of artificial intelligence and science.
While Encode is not exclusively a robotics fellowship, robotics is one of the scientific areas supported through the program. Fellows can potentially work with research labs, datasets, and domain experts across fields including robotics, biomaterials, atmospheric science, neuromorphic computing, protein engineering, and other advanced scientific disciplines.
Encode AI for Science Fellowship
The Encode Fellowship is designed for AI and machine learning researchers, engineers and technical builders who want the freedom to develop tools, systems and early-stage proof points for ambitious scientific applications.
The program is built around the idea that advanced AI should be applied to major scientific and societal challenges rather than being limited to conventional software applications.
According to Encode, fellows receive a year of freedom to build, with access to salary, computing resources, workspace, scientific collaborators and other forms of support.
For robotics-focused researchers, this creates the potential to explore AI applications involving intelligent machines, autonomous systems, scientific instrumentation and other technology-intensive challenges.
Robotics and AI Research Opportunities
Robotics is specifically identified by Encode as one of the areas where fellows can collaborate with research partners and institutions.
The fellowship’s broader scientific focus includes:
- Robotics
- Medicine
- Climate science
- Agriculture
- Physics
- Neuroscience
- Biomaterials
- Atmospheric science
- Neuromorphic computing
- Protein engineering
This interdisciplinary structure is particularly relevant to researchers whose work combines machine learning with engineering, scientific research, or physical-world systems.
The program also allows fellows to propose their own projects or work with existing research opportunities.
What the Encode Fellowship Offers
The fellowship provides substantial resources intended to allow researchers to focus on ambitious technical projects.
According to Encode, the program includes:
- £115,000 salary
- Benefits
- Pension contributions
- Travel stipend
- At least £100,000 in individual computing resources
- Access to datasets and technical tools
- Workspace
- Academic and industry partnerships
- Community events
- Support from experienced builders
- Investor introductions
- Pitch-deck coaching
- IP support
- Continued support after the fellowship
Encode also states that it does not take equity from fellows.
The combination of salary, computing resources and access to scientific collaborators makes the fellowship different from a conventional academic fellowship.
£100,000 Computing Budget
One of the notable features of the Encode Fellowship is its computing support.
The program states that each fellow receives an individual compute budget of at least £100,000.
This resource is designed to reduce the computing constraints that can prevent researchers from developing and testing sophisticated AI models.
For robotics and AI researchers, substantial computing resources can be particularly useful for activities such as model development, simulation, experimentation and evaluation.
Who Can Apply for the Encode Fellowship?
Encode is looking for people with strong technical capabilities in artificial intelligence and machine learning.
The fellowship identifies potential applicants as:
- AI researchers
- Machine learning researchers
- AI engineers
- Software engineers
- Technical builders
- Hackers
- Postdoctoral researchers
- Industry researchers
- Other technically capable applicants
Importantly, a Ph.D. is not required.
Encode states that applicants should be comfortable writing code, working with large datasets, and building and evaluating models. The program also welcomes applicants from different educational backgrounds, including self-taught technical talent.
This makes the opportunity potentially attractive to highly skilled engineers who have developed substantial technical experience outside traditional academic pathways.
Do You Need to Be Based in the UK?
Encode is open to applicants internationally, but fellows need to relocate to the UK for the duration of the fellowship.
The program states that it supports the visa process for successful international applicants.
Therefore, international robotics and AI researchers can potentially apply, provided they are willing and able to relocate to the UK for the fellowship period.
How the Fellowship Works
The Encode Fellowship provides fellows with significant flexibility in determining what they want to build.
Applicants can bring their own project idea or work with Encode to identify and scope an ambitious project.
The program says fellows can collaborate with research partners and institutions for up to 12 months.
Projects may ultimately develop into:
- Open-source tools
- New technical systems
- Scientific research tools
- New institutions or initiatives
- Early-stage ventures
- Startups
The goal is to give technically capable individuals enough freedom and resources to pursue ambitious scientific problems.
Access to Research Labs and Scientific Experts
A major advantage of the fellowship is the opportunity to work across disciplinary boundaries.
Encode says fellows can gain access to laboratories, datasets and domain experts in areas such as robotics, atmospheric science, biomaterials, neuromorphic computing and protein engineering.
This can allow AI specialists to work directly with scientists and researchers who possess domain expertise outside traditional machine learning.
For robotics engineers, this interdisciplinary environment can provide opportunities to apply AI capabilities to scientific and engineering challenges that require more than software development alone.
No PhD Required
One of the most important eligibility points for emerging engineers is that Encode does not require applicants to hold a Ph.D.
The fellowship says applicants can come from different educational backgrounds and that self-taught applicants are welcome.
However, the absence of a Ph.D. requirement does not mean that the fellowship is designed for beginners. The program emphasizes technical ability, coding, working with large datasets, building models, and evaluating those models.
Applicants should therefore be able to demonstrate meaningful technical achievements through projects, research, software, prototypes, publications or other evidence of their capabilities.
Previous Encode Fellows and Scientific Research
Encode’s program materials highlight fellows working on ambitious scientific challenges.
The fellowship’s first cohort included projects connected to areas such as atmospheric systems, neuromorphic computing, cancer prevention and neurotechnology.
Encode says its fellows are working across robotics, medicine, climate science, agriculture, physics, neuroscience and other scientific areas.
This demonstrates the fellowship’s broader objective of bringing AI talent into scientific research rather than restricting participants to traditional AI applications.
Encode Fellowship Application Status
Applicants should note an important timing change.
The Cohort 2 application deadline was March 28, 2026, according to the University of Cambridge opportunity announcement and Encode’s program information. Cohort 2 was scheduled to begin in September 2026.
The current Encode website now states that Cohort 2 applications are closed and invites interested applicants to express early interest in Cohort 3.
Therefore, prospective applicants should not rely on the old March 2026 deadline when planning their application.
How to Prepare for the Next Encode Fellowship
Although the Cohort 2 application period has closed, engineers interested in future cohorts can begin preparing now.
Build a Strong Technical Portfolio
A strong portfolio can help demonstrate your ability to solve difficult technical problems.
Consider documenting:
- Robotics projects
- AI and machine learning systems
- Open-source contributions
- Research projects
- Technical prototypes
- Computer vision projects
- Autonomous systems
- Scientific computing projects
- Large-scale data projects
Demonstrate What You Have Built
Encode is focused on people who want to build.
Applicants should therefore be prepared to demonstrate their technical achievements rather than relying exclusively on academic credentials.
A GitHub portfolio, research project, technical demonstration, or working prototype can provide useful evidence of practical capability.
Develop a Strong Project Idea
Encode allows applicants to propose their own project.
A strong project should identify an important scientific or technological problem and explain how AI could contribute to solving it.
For robotics researchers, this could involve areas such as intelligent systems, autonomous machines, scientific robotics, or AI-enabled physical systems.
Strengthen Cross-Disciplinary Skills
Because Encode operates at the intersection of AI and science, applicants can benefit from understanding the scientific domain in which they want to work.
Being able to communicate effectively with scientists, engineers, and researchers from different disciplines can be valuable in a cross-disciplinary fellowship environment.
Why This Opportunity Matters for Emerging Engineers
Traditional research pathways can sometimes require engineers to choose between academic research and building practical technologies.
Encode attempts to create a different model by combining salary, computing resources, scientific partnerships, and entrepreneurial support.
For robotics and AI engineers, this can create an environment where technical research can move toward practical systems, open-source projects, or new ventures.
The program is particularly relevant to people who have strong technical skills and want to tackle scientific problems with potentially large real-world consequences.
Frequently Asked Questions
Is Encode a robotics fellowship?
Not exclusively. Encode is an AI for Science Fellowship, but robotics is one of the scientific areas supported by the program.
How much does the Encode Fellowship pay?
The program lists a salary of £115,000, along with benefits, pension contributions and a travel stipend.
Does Encode provide computing resources?
Yes. Encode states that fellows receive an individual computing budget of at least £100,000.
Do I need a Ph.D.?
No. Encode explicitly states that a Ph.D. is not required. Applicants can come from different educational backgrounds, including self-taught backgrounds.
Can international applicants apply?
Yes. Encode says it is open to global applicants, but successful fellows must relocate to the UK for the program. The fellowship can assist with visas.
Is the fellowship fully funded?
The program provides a salary, benefits, pension, travel support, computing resources, workspace and other forms of support. It is therefore substantially funded from the fellow’s perspective, although applicants should review the official terms for the complete package.
Is the Cohort 2 application still open?
No. The current Encode website states that Cohort 2 applications are closed. Interested applicants can express early interest for Cohort 3.
Final Thoughts
The Encode AI for Science Fellowship offers an unusual pathway for technically strong engineers and researchers who want to apply artificial intelligence to major scientific challenges.
Although it is not exclusively a robotics program, robotics is among the areas supported by the fellowship, alongside medicine, climate science, agriculture, physics and neuroscience.
The program combines a £115,000 salary, significant computing resources, scientific collaborations, workspace and technical support, while allowing fellows to retain their equity and build projects with substantial independence.
For engineers interested in AI-powered robotics and advanced scientific research, the opportunity is worth monitoring for future cohorts. With Cohort 2 applications now closed, prospective applicants should use the current period to strengthen their technical portfolios, develop ambitious project ideas, and express early interest in the next cohort.
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