Program Description:

Selected faculty will receive support to dedicate focused time to professional development. This can include, but is not limited to, learning fundamental concepts, mastering specific algorithms or models, gaining proficiency with relevant software and hardware, or exploring the application of these fields to novel research questions within their domain expertise. This cycle ICDS is particularly interested in applications for upskilling in AI or Quantum Sciences, but will also consider particularly strong applications related to upskilling in Computational Sciences, Data Sciences, or Digital Twins.

We anticipate that a key benefit for many faculty will be a one-course teaching buyout, which will provide dedicated time for intensive learning and skill development. This teaching buyout can be utilized during Fall 2026, Spring 2027, or Fall 2027. In addition to the teaching buyout, faculty may request funds to support activities that directly contribute to their learning plan and research integration goals. This can include participation in:

• Workshops or training programs on specific tools, techniques, or software to support their upskilling goals.
• Meetings or conferences focused on AI or Quantum Sciences (for faculty from other fields).
• Other activities demonstrating a clear contribution to upskilling in the target areas.

Furthermore, applicants may request credits for compute resources on the Penn State Roar Collab system, funds to access commercial AI tools necessary for their proposed learning and research activities, and/or consulting time from the ICDS RISE team.

Eligibility:

This call is open to tenure-track, tenured, and non-tenure-line faculty at Penn State University, irrespective of their home campus, who have: (i) held a faculty appointment at Penn State for at least three years; and (ii) served as PI for at least one external grant.

Available Support:

• One-course teaching buyout (available for Fall 2026, Spring 2027, or Fall 2027), provided that a letter of support from the relevant Department Head and/or Dean explains how the faculty member’s regular teaching responsibilities will be covered without negatively impacting the university’s educational mission.
• Funds for participation in a meeting, workshop, or other learning activity, provided the application clearly describes how it will support their learning goals.
• Credits for compute resources on the Roar Collab system. See https://icds.psu.edu/services/roar/details-rates/ for current offerings and rates.
• Time from the ICDS RISE team to help set up tools and/or workflows on the Roar Collab system.
• Time from a Campus Champion to advise in the selection of national supercomputing centers and prepare to apply for additional computing resources.
• or accessing commercial AI tools.
• Funds for accessing a quantum computer.

Application Requirements:

Please submit a proposal package including the following components:

• Upskilling Area: Select the most relevant ICDS Research Hub, either AI, Quantum Sciences, Data Sciences, Digital Twins, or Computational Sciences.
• Abstract (1 paragraph): The abstract should: (i) articulate why the applicant seeks to acquire new competencies and how these skills align with their research trajectory; (ii) summarize the technical and learning plans; (ii) explain how the newly acquired expertise will be incorporated into their ongoing or future research programs and future proposals for external funding. The abstract may be made public if your proposal is selected.
• Context & Research Vision (1-2 pages): Provide the context of your current research program and clearly articulate how acquiring new expertise would enhance and potentially transform your existing research. Discuss the anticipated impact on your scholarly output, future research directions, and prospects for external funding.
• Technical Plan (1-2 pages): Provide details of what expertise you plan to acquire during the fellowship period. Include:
• A list of algorithms, models, and/or tools you plan to learn about or utilize.
• A list of specific datasets to be used while building experience in new methods.
• A list of what computing resources will be used.
• Learning Plan (1-2 pages): Describe how you will manage your learning process. Include a timeline with specific objectives and milestones for your learning process. Describe how you plan to integrate your new expertise into your research program. The learning plan might include elements such as:
  • Meeting regularly with a mentor, collaborators, and/or advisees who bring complementary expertise
  • Engaging with a peer network, reading group, or seminar.
  • Practicing reflective journaling, setting accountability milestones, or other strategies that make sense for your specific goals.
• Budget & Budget Justification
  • Specify which semester (Fall 2026, Spring 2026, or Fall 2027) you propose to take the teaching buyout.
  • Cost associated with teaching buyout.
  • Cost and justification for any requested funds for meetings, workshops, or other activities to advance your learning goals. The justification should explicitly address how any travel will contribute to the upskilling goals. Applicants should not request funds for travel to present research results or to meetings in their home discipline.
  • Cost and justification for any requested computing resources at Roar Collab or for commercial AI or Quantum tools and platforms as required by your planned learning and research activities.
  • Hours, areas of expertise, and justification for any RISE consulting requested. Requests for support from the RISE team will be considered as a potential supplement to the primary award. Proposals should be written so they can succeed regardless of whether RISE support is awarded.
• Outcomes from previous ICDS support (if applicable) If the faculty member has received a seed grant from ICDS (e.g., Rising Researcher support, Mid-scale or Super-seed grant, or Upskilling Fellowship), they should summarize key outcomes, including any publications or funding proposals.
• Curriculum Vitae (CV): Include your current CV.
• Letter of Support: A letter from your Department Head and/or Dean is required for any proposals requesting a teaching buyout. This letter must:
  • Confirm their support for your participation in this program if selected.
  • Outline a feasible plan for how your teaching responsibilities will be covered during the teaching buyout period for the specified semester. ICDS will not fund projects that cause the cancellation of a previously planned course.
  • State the estimated cost associated with replacing your teaching for the one course. ICDS will cover the actual replacement cost for course buyouts associated with faculty participation in the ICDS Faculty Upskilling in Artificial Intelligence and Quantum Sciences program. This may differ from the buyout model used in external grant applications (e.g., a percentage of faculty salary). The intent is to fund the actual costs for the department to backfill the faculty member’s regular teaching (e.g., hiring an adjunct, instructor, or graduate student).

Optional Supporting Documents:

• Additional Letters of Support: If the Learning Plan includes a mentor or collaborators, then faculty should provide letter(s) of support from the prospective mentor and/or any key collaborators confirming that they are willing and able to contribute as described in your learning plan if selected.
• Curriculum Vitae (CV) and/or Publication of Mentor/Collaborators: If the Learning Plan includes a mentor or key collaborators, then the faculty are allowed to provide a CV and/or publication list from the proposed mentor/collaborators to establish the credibility of the learning plan.

Applications will be evaluated based on the following criteria:

• Quality of the Technical and Learning Plans: The clarity, feasibility, and ambition of the proposed learning plan, including the suitability of the chosen tools, techniques, or areas of study for the stated goals and the proposed timeline for the teaching buyout.
• Anticipated Impact on Research Program: The potential for the acquired knowledge and skills to significantly enhance the applicant’s research program, leading to new discoveries, methodologies, or research directions.
• Anticipated Benefits to Future External Funding Proposals: The likelihood that this professional development will strengthen the applicant’s ability to secure significant external research funding in the future.
• Track Record and Potential:
  • For Assistant & Associate Professors: Proposals will be evaluated on their potential for attracting significant research funding in the future and demonstrating a clear trajectory for growth.
  • For Full Professors: Proposals will be evaluated on their demonstrated track record of securing external funding, how this opportunity would complement their existing research program, and how it would enable them to pursue new, high-impact research avenues.
• Departmental Support: The strength of the Department Head’s letter, including a clear plan for teaching replacement for the proposed semester, and confirmation of the associated cost.

Successful applicants will be expected to:

• Participate in 4 meetings with other faculty participating in the Faculty Upskilling Fellowship program with goals of identifying common challenges, supporting each other’s learning, and improving the program in future semesters.
• Submit a ~2 page final report within one year of the completion of the award period, summarizing: the activities undertaken, what was learned and/or the skills acquired, how the faculty member is/will apply them to their research, and the short-term and/or projected long-term impact on their research program highlighting any upcoming grant applications or new research directions that benefited from this program.
• Work with the ICDS Communications team to contribute a short news story/blog post about their experience with the Upskilling Fellowship program.
• Either: (i) Give an ICDS seminar within one year of completion to share their learning experience and its impact on their research with the broader Penn State community; or (ii) Work with ICDS to deliver a hands-on workshop to help train other Penn State researchers in skills developed during their fellowship.
• Contribute learning materials that they developed or links to learning materials that they found particularly useful to a public, ICDS-maintained Learning Repository targeting their peers and other members of the ICDS community.
• Notify ICDS if/when they have external funding successes that were enabled by their fellowship.

 

Submission Process:

Proposals must be submitted electronically through the Penn State InfoReady portal. Link: http://psu.infoready4.com/#freeformCompetitionDetail/2001804

All application materials must be submitted via InfoReady by 5:00 PM ET on Feb 11, 2026.

Applicants are encouraged to contact the most relevant ICDS Hub Director to discuss their application ideas prior to submission.

Questions regarding this call for proposals should be directed to ICDS-FACULTY-UPSKILLING@LISTS.PSU.EDU. ICDS will update the FAQ with responses to common questions.

We look forward to receiving your proposals and supporting your growth in these critical areas of research.

Frequently Asked Questions:

Q: Is this call intended for faculty new to AI, Digital Twins or Computational/Data/Quantum Sciences to get started? Or for faculty who already have significant expertise to build even deeper expertise?
A: This call is open to faculty at any career stage and at any stage of their development of skills in AI, Digital Twins or Computational/Data/Quantum Sciences or to enhance their research programs. Faculty with existing expertise should clearly describe the areas in which the fellowship would allow them to grow their expertise substantially more than is possible without the fellowship.

Q: Is this call intended for faculty to apply newly developed skills to existing research projects or to new problems?
A: This call is intended for faculty to up their game in any of AI, Computational Sciences, Data Sciences, Digital Twins or Quantum Sciences to enhance their research program going forward, rather than to fund a specific research project using either. Given the breadth of ICDS Research Hubs, identifying a goal and specific objectives would likely help to focus the learning plan.

Q: Would developing code or tools related to AI/Quantum Sciences fall within the scope of this program?
A: Faculty may include writing code or software tools as part of their learning plan, keeping in mind that this program is primarily designed to support faculty in growing their expertise, rather than a specific research project.

Q: How do you define AI?
A: For this call, AI (and Quantum) should be interpreted broadly. For example, you could refer to point to “I.A. Definition of AI” from the most recent NSF call for National AI Research Institutes. In practice, ICDS is defining AI broadly, so faculty can drive what areas of AI (and Quantum Sciences) are most relevant to them.

Q: Why do you require a letter of support for proposals requesting a teaching buyout?
A: ICDS can’t approve a teaching buyout itself. We must work as partners with the relevant Department Head and/or Deans (depending on the unit). In some cases, there may be challenges, particularly in light of the new university budget model that closely links student credit hours to funding for colleges. Therefore, ICDS will require that the letter of support from your Dept Head/Dean convey that they support the idea and address how your usual teaching responsibilities would be covered. Your Department Head and/or Dean is best positioned to understand the specific challenges that your unit faces and develop a plan that allows the university to fulfill its teaching mission while also investing in the long-term professional development of its faculty. To allow enough time to make such plans, we’re targeting this call for buyouts in Fall 2026, Spring 2027, and Fall 2027.

Q: Are faculty required to have a mentor or collaborator?
A: No. Incorporating a mentor or collaborator into your learning plans might make sense and increase the competitiveness of your proposal. However, faculty should propose a learning plan that makes the most sense for their unique circumstances.

Q: Does ICDS provide a mentor for successful applicants?
A: No. Applicants are responsible for identifying and requesting letters of support from prospective mentors.

Q: Is this call intended for faculty to incorporate AI, Computational Sciences, Data Sciences, Digital Twins, or Quantum Sciences into their teaching?
A: No. While there may be ancillary benefits for teaching, this ICDS call is designed to help faculty build skills that will support their research programs.

Q: Should faculty request all of the available forms of support?
A: No. We anticipate that most applications will request a teaching buy-out and one or two of the other potential forms of support. Faculty should decide which forms of support would be most appropriate for their goals and learning plan.

Q: May faculty apply to be part of this program if they do not request a teaching buyout?
A: Yes. Such applications may request alternative resources of comparable scale to support their upskilling.

Q: May faculty on a 36-week contract request “summer salary” from this program?
A: No.

Q: Is this program appropriate for faculty from fields not usually associated with AI, Digital Twins, or Computational, Data, and Quantum Science research?
A: Yes. Faculty are encouraged to apply irrespective of their formal training or home department. The technical and learning plans may differ substantially depending on the faculty member’s previous experience, primary discipline, and goals.

Q: Can you provide some examples of how faculty might use this program?
A: The following examples are meant to help faculty get started brainstorming and are not intended to be complete.

• A faculty member from the Eberly College of Sciences mastering Machine Learning methods for analyzing quantum data and gaining expertise in neural network-based wavefunction representations to study complex quantum phases in moiré materials.
• A faculty member from the College of Education developing proficiency in transformer-based language models and prompt engineering techniques for educational content generation and mastering generative AI approaches for creating realistic scenarios and training pedagogical AI tutors.
• A faculty member from the College of Engineering gaining foundational understanding of Recurrent Neural Networks and Long Short-Term Memory networks for time series analysis, Principal Component Analysis for signal compression, and Reinforcement learning approaches to model intelligent control systems.
• A faculty member from the College of Liberal Arts learning to improve their ability to integrate Large Language Models into their research and into survey platforms for human subjects research testing, with a focus on iterative interactions between human participants and chatbots.
• A faculty member from the College of Medicine building applied technical expertise in supervised and unsupervised learning methods, model interpretation techniques, and large-scale data handling so they can evaluate policy whether ML models perpetuate or mitigate health disparities.
• A faculty member from the Statistics department building foundational knowledge in quantum computing and exploring its potential intersection with machine learning to examine how quantum kernel methods, variational quantum circuits, or quantum-inspired optimization could be applied to robust clustering and optimal transport.

Q: What is a Campus Champion?
A: The RISE team includes multiple “Campus Champions” who are familiar with options for supercomputing resources at Penn State and at national supercomputing centers and programs such as ACCESS (formerly XSEDE). Faculty can request their assistance in selecting which facilities are likely to be a good fit for their needs, requesting small starter allocations to benchmark their codes, and preparing applications for additional supercomputing resources.

Q: What areas of expertise are contained in RISE?
A: RISE team members have experience in computer programming, including code optimization, parallel programming, GPU programming, software engineering, and developing computational notebooks (e.g., Jupyter, RStudio). RISE team members also have experience applying AI and Machine Learning techniques (e.g., regression, classification, clustering, decision trees, neural networks) in multiple domains. As methodology gets more specialized (e.g., convolutional neural networks, natural language processing), fewer RISE team members have significant experience, and those who do may already be reserved. Therefore, requests for support from the RISE team will be handled separately (and on a different timeline) from the rest of this program. We encourage faculty to identify potential ways a RISE team member could support their proposal, but to ensure their learning plan is successful, even if a RISE team member is not available to assist. See https://icds.psu.edu/services/rise/ for more information.