Jobs · PhD positions
AIM PhD Fellowships
The Vermont Complex Systems Institute has received funding from the National Science Foundation to support six fully funded PhD fellowships at the intersection of AI, Machine Learning and Data Science (AIM Fellows). Each fellow will become part of a VCSI lab and will be paired with an industry partner working with that lab.
The fellowships provide an opportunity to explore research at the intersection of academia and industry.
Who can apply
To be eligible, applicants must have received honorable mention status after applying to the NSF Graduate Research Fellowship Program (GRFP).
Applicants who meet GRFP honorable mention criteria can apply for these fellowships through the NSF ETAP platform.
Apply through NSF ETAPSix fellowships
Learn more about each fellowship opportunity.
Fellowship 01
Computational Ethics Lab + Google
The Computational Ethics Lab is a post-disciplinary research group at the Vermont Complex Systems Institute working at the intersection of complex systems, computational social science, and philosophy. The lab studies the ethical dimensions of emerging technology using natural language processing, network science, mechanistic interpretability, and qualitative methods. Current research asks what stories large language models learn and distort, where individual consent breaks down in networked systems, and how human bias shapes the perception and detection of AI-generated content.
Through this fellowship, the student will work on research on how AI and machine learning are reshaping open-source ecosystems, including questions about AI-generated contributions, trust between maintainers and contributors, and the health and sustainability of the communities that build shared software infrastructure. The fellow will be advised by Dr. Lovato.
Advisor
Dr. Juniper Lovato is Assistant Professor of Computer Science at the University of Vermont, a core faculty member at the Vermont Complex Systems Institute, and Director of the Computational Ethics Lab. Her expertise is at the intersection of complex systems, computational social science, survey development and analysis, natural language processing, ethical philosophy, and socio-technical systems research. Her work has been funded by NSF, the Alfred P. Sloan Foundation, and Google Open Source. Students interested in working with Google will join the Computational Ethics Lab to explore AI/ML applications in open-source ecosystems.
Industry partner
The Google Open Source Programs Office (OSPO) was founded in 2004, making it one of the first in the industry. It began by enabling Google to build on open-source technologies and to release Google-developed technology under open licenses, and expanded with the 2005 launch of Google Summer of Code to support mentorship and lower barriers to participation.
Today the office runs programs aimed at improving the open-source ecosystem as a whole, including Season of Docs and efforts on open-source security. It also helps Google-led projects grow, among them Android, Chromium, and Go, and Google partnered with the Linux Foundation to create the Cloud Native Computing Foundation, which hosts Kubernetes. Google remains committed to supporting open-source projects, communities, and maintainers across the entire ecosystem.
Fellowship 02
Computational Story Lab + BETA
The Computational Story Lab, co-directed by Chris Danforth and Peter Sheridan Dodds, works at the basic science level on complex sociotechnical systems. The lab studies how people, language, and stories move through and shape sociotechnical systems. Housed within the Vermont Complex Systems Institute, the lab combines large-scale data collection and analysis, sociotechnical experiments, and the formulation and simulation of theoretical models to examine contagion, meaning, emotion, and narrative across social media, literature, news, and other large text corpora. The lab's work also spans the measurement of individual and collective health and well-being, the structure of essential meaning, the evolution of stories and characters, and it has a long history of creating open measuring instruments for data-rich systems for use by researchers and the public. The lab's research has been supported by NSF, NIH, NASA, ONR, the MITRE Corporation, MassMutual, and Google, and has drawn regular coverage from venues including the BBC, the New York Times, the Washington Post, and National Geographic.
Advisor
Dr. Chris Danforth is Professor of Mathematics & Statistics, Director of the Vermont Advanced Computing Center, and co-Director of the Computational Story Lab. He works in applications of data science to health, with a focus on mental health, wearable devices, and social media. Dr. Danforth has published over 100 peer-reviewed studies and co-advised more than 50 graduate students. Dr. Danforth was recognized as a University Scholar in 2024, honoring his significant academic contributions to the University of Vermont.
Industry partner
BETA is a Vermont-based aerospace company designing, manufacturing and selling high-performance electric aircraft, advanced electric propulsion systems, components and charging systems to top operators worldwide. BETA has built and flown its family of ALIA aircraft, consisting of both conventional fixed-wing electric aircraft and electric vertical takeoff and landing aircraft, more than 200,000 nautical miles, including multiple trips across the United States.
BETA is deploying a network of charging infrastructure across the United States and internationally to enable the growing industry. BETA's intentional approach to developing the enabling technologies necessary to electrify aviation allows BETA to serve a customer base across cargo and logistics, defense, passenger and medical end markets and unlock cost-effective and safe missions. BETA is a publicly traded company listed on the New York Stock Exchange.
Fellowship 03
Computational Story Lab + Wikimedia
The Computational Story Lab, co-directed by Chris Danforth and Peter Sheridan Dodds, works at the basic science level on complex sociotechnical systems. The lab studies how people, language, and stories move through and shape sociotechnical systems. Housed within the Vermont Complex Systems Institute, the lab combines large-scale data collection and analysis, sociotechnical experiments, and the formulation and simulation of theoretical models to examine contagion, meaning, emotion, and narrative across social media, literature, news, and other large text corpora. The lab's work also spans the measurement of individual and collective health and well-being, the structure of essential meaning, the evolution of stories and characters, and it has a long history of creating open measuring instruments for data-rich systems for use by researchers and the public. The lab's research has been supported by NSF, NIH, NASA, ONR, the MITRE Corporation, MassMutual, and Google, and has drawn regular coverage from venues including the BBC, the New York Times, the Washington Post, and National Geographic.
Advisors
Dr. Peter Dodds is Professor of Mathematics & Statistics and Director of the Vermont Complex Systems Institute. He co-runs the Computational Story Lab with Chris Danforth. He was named a Fellow of the Network Science Society in 2022, was honored as a UVM University Scholar in 2019, and is an external faculty member of the Santa Fe Institute and the Complexity Science Hub in Vienna.
Dr. Chris Danforth is Professor of Mathematics & Statistics, Director of the Vermont Advanced Computing Center, and co-Director of the Computational Story Lab. He works in applications of data science to health, with a focus on mental health, wearable devices, and social media. Dr. Danforth has published over 100 peer-reviewed studies and co-advised more than 50 graduate students. Dr. Danforth was recognized as a University Scholar in 2024, honoring his significant academic contributions to the University of Vermont.
Industry partner
The Wikimedia Foundation works towards the vision of a world in which every single human being can freely share in the sum of all knowledge. They host a technology infrastructure that makes possible billions of monthly visits to Wikipedia, and are collaborating with the Computational Story Lab at UVM to understand collective attention through language-based instruments like the prototype at wikipedia.uvm.edu.
Fellowship 04
Joint Lab + Agora Intelligence
Advisor
Dr. Jean-Gabriel Young is Assistant Professor of Mathematics and Statistics at the University of Vermont, a core faculty member at the Vermont Complex Systems Institute, and co-Director of the Joint Lab. His interdisciplinary research intersects computational statistics, AI, complex systems, and forecasting. He has written over 45 peer-reviewed articles, mentored 12 graduate students and postdocs, and actively contributes to various open-source projects and scientific communities.
Dr. Young also works with several industry partners, including Google, Hive.one, and Agora Intelligence. Students interested in working with Dr. Young will have the opportunity to be paired with industry mentors from Agora Intelligence exploring applied generative AI research in the Joint Lab.
Industry partner
Agora Intelligence is the maker of Tilt, a platform for investment research and custom indexing. Tilt helps users translate ideas and emerging trends into baskets of public companies. Its natural-language interface converts user requests into structured inputs for a rules-based methodology engine. The company aims to make AI-assisted investment decisions auditable and reproducible.
Fellowship 05
Neurobotics Lab + Cognizant AI Labs
The UVM Neurobotics Lab investigates bio-inspired machine learning approaches to produce more robust, adaptable, and efficient AI models. The lab is inspired not by specific instances of biological intelligence, but by the underlying processes that evolve, learn, develop, and grow naturally intelligent systems, with the intent to better understand and replicate these fundamental processes to increase our understanding of the natural world and our ability to recreate it for engineering purposes.
The lab has a particular specialization in open-endedness: creating AI systems that continually improve and diversify like a branching evolutionary process, instead of training that converges on a single solution like most modern AI systems. The fellow will investigate open-endedness on topics shared between the UVM Neurobotics Lab and Cognizant AI Labs, which may include recursive self-improvement, agentic systems with multi-agent teams, quality-diversity, and evolutionary computation.
Advisor
Dr. Nick Cheney is Associate Professor of Computer Science and Director of the Neurobotics Lab. His foundational research has won awards from a wide variety of AI conferences and professional societies. He is the winner of an NSF CAREER Award, the ACM SIGEVO Impact Award for seminal contributions to the field of Evolutionary Computation, the UVM Provost's Award for Excellence in Doctoral Mentoring, and the UVM Inventor Hall of Fame Award for success in translating basic research to real-world commercial use.
Dr. Cheney has published over 100 peer-reviewed studies, advised 14 graduate students, and has ongoing collaborations with numerous industry partners. He has led over $15M in funding as a PI and been involved in over $55M in funding from the NSF, DARPA, Army, USDA, NIH, NASA, Google, MassMutual, and the Alfred P. Sloan Foundation. His award-winning scientific communication videos have over 1 million views, and his work has been highlighted in media outlets like Wired, Popular Science, and TED.
Industry partner
The fellow will work in close collaboration with Risto Miikkulainen, VP of AI Research at Cognizant AI Lab and Professor of Computer Science at the University of Texas at Austin.
Cognizant AI Lab is a global R&D organization advancing fundamental AI research and real-world systems. Their mission is to advance the science, engineering, and governance of autonomous systems to strengthen the future of business, societal resilience, and trust in AI. Through peer-reviewed research, open-source platforms, AI for Good initiatives, and collaborations with universities and partners, they help build a future where people and autonomous AI can create, trust, and grow together.
Fellowship 06
PLAID Lab + MassMutual
This fellowship will support one fully funded Computer Science PhD student in the PLAID Lab under the supervision of Dr. Joe Near for the duration of their PhD studies. The PLAID Lab at the University of Vermont conducts research in cybersecurity and data privacy, using techniques from programming languages and formal methods, AI, and HCI. Research projects have included development of approaches to privacy-preserving data analytics and machine learning, formal verification of cryptographic protocols and differential privacy, modeling and verification of security properties in distributed systems, and AI-based network traffic analysis for malware detection.
Advisor
Dr. Joseph Near is an Associate Professor in the Computer Science department at the University of Vermont. His primary research areas include cybersecurity and privacy, along with AI and programming languages. He is a core faculty member of the UVM Center for Computer Security and Privacy and the PLAID Lab, and an Affiliate of the Vermont Complex Systems Institute. Dr. Near's research has been funded by NSF, DARPA, IARPA, and industry collaborations, and includes work in applied cryptography, differential privacy, type systems and formal methods, human-computer interaction, and privacy-preserving machine learning.
Students interested in working with Dr. Near will have the opportunity to collaborate with MassMutual on security, privacy, and AI-related projects. This collaboration has included research on new cryptographic protocols for distributed machine learning, and on threat modeling for network traffic analysis and data exfiltration.
Industry partner
MassMutual is a Fortune-500 life insurance company that employs more than 10,000 people worldwide. MassMutual's AI & Data Science team brings together experts in applied AI, statistics, and computer science, operating at the intersection of cutting-edge research and enterprise delivery. The team designs, builds, and deploys AI solutions to complex, high-value business problems, drawing on machine learning, LLMs, generative and agentic AI, and probabilistic modeling, with work that shapes the future of MassMutual and the life insurance industry more broadly.
It partners closely with technology and business stakeholders across the enterprise, and it invests in growth through a culture of peer learning, candid feedback, and shared technical standards, grounded in a commitment to scientific and engineering excellence.