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IAPS AI Policy Fellowship

Job Description: A fully-funded 3‑month professional fellowship (Sep–Nov 2025) run by the Institute for AI Policy and Strategy (IAPS) to strengthen practical AI policy skills for participants from industry, government, or academia.

Role Description: Fellows are matched with an AI policy expert and collaborate on real-world AI policy projects (e.g. writing policy papers, organizing convenings, briefing officials). They work full-time (40h/wk), develop outputs with their mentor, and learn about national/global AI policy issues. The program includes a 2-week in-person residency in Washington, D.C., after which fellows may continue on-site (if local) or remotely.

Location: Washington, D.C. (with option to participate remotely worldwide). All fellows gather in D.C. for a required residency (Sept 1–12).

Application Cycle: Annual (runs each year, Sep–Nov). .

What You’ll Need:

- At least a bachelor’s degree (professionals and advanced students welcome).
- Resume/CV and transcripts (if academic).
- Application essays (the online form asks about your background, project interests, and how you’d use the $2K project grant).
- (For D.C. option) U.S. work authorization during the program (they cannot sponsor visas).

Preferred/Required Qualifications:

- Demonstrated interest in AI and policy (backgrounds from tech, think tanks, government, etc. are encouraged).
- Experience or coursework related to AI, public policy, or national security.
- Strong written and analytical skills.

Benefits and Perks:

- Competitive stipend: $22,000 (for Senior Fellows) or $15,000 (Fellows) for the 3-month program.
- $2,000 project grant to support a research deliverable.
- Career coaching from IAPS staff, weekly mentorship, and networking with AI policy experts.
- Experience working on high-impact AI issues (e.g. AI procurement, national security, ethics) alongside top researchers.

Where and How to Apply: Applications open once a year on the IAPS website. Prospective applicants should apply via the online portal at iaps.ai/fellowship when announced. (Email newsletter for updates.)

Postdoctoral Research Associate – Yale Clinical Neurosciences Imaging Center

The Yale Clinical Neurosciences Center (CNIC), housed within the world-class Department of Neurology at Yale School of Medicine, provides an integrated core for collaborative research on disorders of the nervous system using state-of-the-art imaging facilities at the Yale Magnetic Resonance Research Center, Yale Positron Emission Tomography Center, and Yale New Haven Hospital. The Fredericks Lab is one of the core CNIC research labs, which are collectively dedicated to improving care for nervous system disease through cutting-edge research and educational events.

Overview of the Position:

The postdoctoral research associate will have the opportunity to take on leadership and key analytic roles in our neuroimaging studies of early-stage and clinically heterogenous neurodegenerative disorders. Our lab uses a multimodal neuroimaging approach (including functional and structural MRI and targeted PET imaging) to better understand how Alzheimer’s disease (especially less common variants that disproportionately impact language and spatial function) and alpha-synuclein (a protein that causes Parkinson’s disease and Dementia with Lewy Bodies) affect brain circuits. The postdoc’s work will encompass studies that are actively collecting data as well as applications to the HCP-A, A4, ADNI, and other publically available datasets focused on aging and neurodegeneration.

The postdoc will receive training in state-of-the-art neuroimaging analysis methods, including quantitative structural analysis, network-based functional analysis, voxelwise cross-modality analyses, graph theoretic approaches, and machine-learning based applications for functional neuroimaging such as connectome-based predictive modeling. They will also receive training in the clinical neuroscience of preclinical and early-stage disease and in heterogenous Alzheimer’s and alpha-synuclein phenotypes. In addition to the analysis of neuroimaging data, they will receive extensive experience in scientific writing, with the aim of generating both high-impact publications and creative new ideas for the lab. Our lab works collaboratively across disciplinary lines, with ongoing projects in collaboration with radiologists at the Yale MRRC (Eve Lake, Todd Constable) and PET Center (Nicholas Guehl, Georges el Fahkri) and colleagues at Yale Psychiatry (Al Powers, Chris van Dyck) and the postdoc will have the opportunity to work collaboratively with these research teams.

The postdoc’s program of training will also include both attendance at departmental workshops and seminars. They will present their results at weekly Fredericks Lab meetings, join in our weekly journal club, and be expected to present their work at top national and international conferences in our field, such as Alzheimer’s Association International Conference, Society for Neuroscience, Alzheimer’s Disease/Parkinson’s Disease, and Human Amyloid Imaging.

Location: CB643 (Clinic Building), Yale Medical Campus, New Haven, CT

Qualifications:

• Applicants should have recently completed or be about to complete a relevant PhD program. T

• The ideal applicant will have prior experience in neuroimaging analysis and a strong background in Python, Matlab, &/or R, and/or facility in other scripting languages and the willingness to learn these.

• Interpersonally, they should have outstanding communication and teamwork skills and be comfortable leading a team of graduate students and other trainees, yet able to seek direction/assistance when needed.

• They should demonstrate strong initiative and ability to problem-solve independently and learn new software programs, as well as strong organizational and time management skills. • An excellent working knowledge of the English language, including scientific terminology, is required.

• Experience with large public neuroimaging datasets such as HCP or ADNI, patients with neurodegenerative diseases, functional connectivity, graph theoretic, &/or machine-learning based approaches to imaging analysis are all a plus.

• There will be a significant amount of flexibility based on the post doc’s interests and goals, and motivation and enthusiasm are more important than specific prior experiences.

Process to Apply:

Applications will be accepted on a rolling basis until the position is filled.

If interested, please send a cover letter, CV, and the names/contacts for three references to carolyn.fredericks@yale.edu and Fredericks.lab@yale.edu.

Post-Doctoral Position in Deep Learning for MRI Reconstruction

Position Description:

Join an exciting effort to develop a low-field, low-cost, MRI scanner for screening mammography. You will participate in the development of MRI reconstruction algorithms for the scanner using machine learning and deep learning.

Qualifications:

The position requires some background in machine learning, optimization, and deep learning. Some familiarity with MR physics, with PyTorch and/or other GPU programming tools is also necessary. You should have completed all requirements for your PhD by the time you are hired.

How to Apply:

Candidates who have most, but not all, of the necessary background are encouraged to apply. Please email a CV and a cover letter to one of the following:

Prof. Hemant Tagare (hemant.tagare@yale.edu)

Prof. Todd Constable (todd.constable@yale.edu)

Prof. Gigi Galiana (gigi.galiana@yale.edu)

Postdoctoral Associate Position on Blockchain and Machine Learning

About the postdoctoral project

We are seeking highly motivated postdoctoral researchers in biomedical informatics and data science to work on projects focused on the development of biomedical blockchain, clinical predictive models, and blockchain-based privacy-preserving learning. The focus will be blockchain technology and machine learning, and candidates with at least one of the backgrounds are welcomed to apply. This position is 100% on site in New Haven, Connecticut.

Candidate requirements

Candidates must have a PhD in Biomedical Informatics, Data Science, Computer Science, or an informatics/engineering-related field (e.g., Health Informatics, Clinical Informatics, Biomedical Engineering, Electrical Engineering). Those in the final stages of their PhD are also eligible to apply. A good understanding of medical data and/or electronic health records is considered an asset. The successful candidate should be capable of working both independently and as part of a team.

Essential skills

Proficiency and hands-on experience in blockchain technology (preferably Ethereum) and/or machine learning.

Solid foundation in computer network and/or data analysis.

Strong programming skills, preferably in Java.

Ability to work collaboratively in an interdisciplinary team.

Good to have

Experience in smart contract development, preferably in Solidity.

Familiarity with cloud computing and shell script.

Knowledge of biomedical blockchain, clinical predictive models, and/or blockchain-based privacy-preserving learning.

Good understanding of medical data and/or electronic health records.

Soft skills

Excellent English, both written and spoken, and strong communication skills.

Ability to work effectively in teams.

Ability and interest in mentoring junior scientists.

Strong problem-solving and critical-thinking skills.

Good time management and organizational skills.

Ability to work independently and take initiative.

About the Supervisor

The primary supervisor for the project is Dr. Tsung-Ting Kuo, an Associate Professor of Biomedical Informatics & Data Science and of Surgery at the Yale School of Medicine. Dr. Kuo is experienced in biomedical blockchain and predictive modeling field with 90+ publications and 3,800+ citations in Google Scholar. Dr. Kuo was awarded a NIH K99/R00 Pathway to Independence Award and will be happy to mentor postdocs for their career award application. Our current project can accelerate the development of methods/technologies to increase willingness of institutions to participate in such a collaboration for improving the effectiveness of healthcare. Our postdocs also have opportunities to connect with other postdocs in our department to build a network of junior researchers. More about Dr. Kuo can be found here at: https://medicine.yale.edu/profile/tsung-ting-kuo/.

About the department

The Department of Biomedical Informatics & Data Science (BIDS) is a new department at the intersection of health sciences and information technology. It develops new approaches to organize and analyze biomedical and healthcare data to promote health for all. More information about BIDS can be found here at: https://medicine.yale.edu/biomedical-informatics-data-science/

Additional Information

A Postdoctoral Associate position is an initial appointment of one year renewable upon satisfactory progress. Information on compensation and benefits can be found athttps://postdocs.yale.edu/yale-postdoctoral-positions andhttps://postdocs.yale.edu/postdocs/benefits, respectively. This is not a remote opportunity. The position is located at the Yale BIDS building located in New Haven, CT. In accordance with university requirements, postdoctoral trainees in this position are expected to be on campus full time for in-person training.

How to Apply

Please send your CV to Dr. Kuo tsung-ting.kuo@yale.edu directly.