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Position Overview / Introduction to Lab / Center / PI

The Artificial Intelligence Exploration Center at SUNY Polytechnic Institute invites applications for a Post-Doctoral Associate to research on the generation and analysis of sequential data, particularly in the domain of Positioning, Navigation, and Timing (PNT). The research aims to generate and evaluate the quality of synthetic PNT data created using pre-trained foundation models (FMs) such as Large Language Models (LLMs). The position will also involve creating quantitative evaluation frameworks to assess the quality, realism, and reliability of generated data, as well as integrating graph-based and network analytic approaches, such as visibility graph analysis, to uncover latent patterns, detect anomalies, and characterize dynamical behavior. This position offers an exciting opportunity to work at the intersection of AI, physics, data science, and systems engineering, contributing to the development of robust and verifiable synthetic data pipelines for next-generation analytical and decision-support systems.

The successful candidate will join a multidisciplinary research team comprising mathematicians, physicists, engineers, and other faculty members at SUNY Polytechnic Institute. The position is based in Utica, NY.

Job Responsibilities

+ Collaborate with the project's Principal Investigators to design and implement generative AI models for sequential PNT data.

+ Develop and fine-tune foundation models for synthetic data generation: Time Series Foundation Models, Variational Autoencoders, Generative Adversarial Networks, and Large Language Models (LLMs, TSFMs, VAEs, GANs).

+ Design experiments to test synthetic data quality, reliability, and resilience under corrupted or adversarial conditions.

+ Participate in compiling and curating large-scale datasets for model training and benchmarking.

+ Develop and document analytical tools and quantitative metrics for comparing real and synthetic PNT data.

+ Construct network-based representations of time series data to analyze structural and temporal dependencies.

+ Contribute to the preparation of publications, technical reports, and conference presentations.

+ Engage in proposal writing and outreach activities that expand the project's scope and impact.

Salary : $60,000 - $65,000

Job Requirements:

Minimal Qualifications:

The successful candidate will hold a doctoral degree in Computer Science, Data Science, Physics, Applied Mathematics, Electrical Engineering, or a closely related field.

Required Skills :

Proficiency in Python/C and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or Keras).

Interest in or prior experience with generative AI methods such as GANs, VAEs, transformers, or foundation models (LLMs, TSFMs).

Ability to design and conduct computational experiments, including model training and evaluation.

Experience with data analysis, visualization, and quantitative assessment of model outputs.

Strong communication and writing skills, with an interest in collaborative, interdisciplinary research.

Preferred Skills:

Background in signal processing, network science, or statistical physics applied to time series and/or complex systems analysis.

Familiarity with PNT data, spatiotemporal datasets, or related domains.

Experience with graph-based data analysis or anomaly detection methods.

Exposure to high-performance or GPU-based computing environments.

Demonstrated ability to contribute to publications or technical reports.

Additional Information:

Additional Information:

Research Foundation for SUNY Polytechnic Institute offers exceptional benefits such as healthcare, dental, vision, pension plans, competitive pay, generous paid time off, tuition assistance, life insurance and long-term disability insurance.

As an Equal Opportunity / Affirmative Action employer, Research Foundation for SUNY will not discriminate in its employment practices due to an applicant's race, creed, religion, color, citizenship, national origin, sex, age, sexual orientation, predisposing genetic characteristics, gender identification or expression, genetic information, familial status, marital status, pregnancy, status as a domestic violence victim, criminal conviction, disability, military status, disabled veteran, recently separated veteran, Armed Forces Service Medal veteran, active duty or wartime campaign badge veteran, or other characteristic as protected by law. Please feel free to review your equal employment opportunities protections and laws pertaining to these protections at http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf

The Company will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.

Review of applications will begin immediately and continue until the position is filled.

Research Foundation employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1. You will be asked to disclose any such participation at the time of hire for review by the Research Foundation. The full text of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486-1-border/@@images/file