Postdoctoral Research Associate

Position Function

The National Strategic Planning & Analysis Research Center (NSPARC) at Mississippi State University is a trusted source for inspired research, advanced analytics, and software architecture and development. NSPARC conducts interdisciplinary work across workforce and economic development, education, health, human services, corrections, and related public-interest domains.

NSPARC seeks a highly motivated postdoctoral researcher to design, develop, and evaluate artificial intelligence and machine learning methods for complex, multimodal datasets. The successful candidate will work with combinations of structured and unstructured data, including tabular and administrative records, longitudinal and time-series data, text, images, and other sensor or digital data, and translate research questions into reproducible analytical pipelines, validated models, scholarly products, and decision-support tools.
The role is well suited to a researcher who can move fluently from data engineering and exploration analysis to model development, rigorous evaluation, interpretation, and communication. The candidate will collaborate with research faculty, scientists, software engineers, data professionals, and students, while demonstrating initiative and intellectual independence.

UC

Please see Staff Compensation Structure or Skilled Crafts and Service Maintenance Compensation Schedule for salary ranges. For salary grade UC, these positions are "Unclassified" and salary ranges are determined by the hiring department.

• Multimodal research:
  Develop AI/ML approaches that integrate and analyze tabular, time-series, textual, image, and other data modalities.
• Data engineering:
  Acquire, clean, harmonize, link, transform, document, and quality-check large and heterogeneous datasets; build reusable and auditable data pipelines.
• Time-series modeling:
  Conduct temporal and longitudinal analyses, including forecasting, anomaly detection, sequence modeling, and appropriate validation under temporal dependence.
• Computer vision:
  Develop and evaluate models for image classification, object detection, segmentation, representation learning, and related vision tasks.
• Language and multimodal AI:
  Build and assess natural language processing, large language model (LLM), vision-language model (VLM), embedding, retrieval-augmented generation, and multimodal fusion workflows when appropriate to project goals.
• Model evaluation:
  Design rigorous experiments; select meaningful baselines and metrics; assess generalization, calibration, uncertainty, robustness, bias, fairness, and error patterns.
• Research translation:
  Convert analytical results into clear reports, visualizations, presentations, manuscripts, proposals, technical documentation, and actionable recommendations for technical and nontechnical audiences.
• Statistical and descriptive analysis:
  Conduct rigorous descriptive and traditional statistical analyses, including data summarization, estimation, hypothesis testing, regression, analysis of variance, and other appropriate inferential methods; clearly interpret assumptions, effect sizes, uncertainty, and practical significance
• Collaboration:
  
  Contribute to interdisciplinary project planning, mentor or support students and junior team members as appropriate, and coordinate effectively with internal and external collaborators.
• Additional duties may be assigned consistent with the knowledge, skills, and abilities required for the position.

• A PhD in Computer Science, Artificial Intelligence, Machine Learning, Electrical or Computer Engineering, Statistics, Applied Mathematics, or a closely related field.

• Hands-on experience with PyTorch, Tensor Flow, scikit-learn, or comparable machine-learning frameworks.
• Experience with multimodal learning, representation learning, data fusion, or joint modeling across structured data, time series, text, images, video, audio, or sensor data.
• Experience developing computer vision systems using convolutional neural networks, vision transformers, foundation models, or related architectures.
• Experience adapting, fine-tuning, evaluating, or deploying LLMs and VLMs; familiarity with prompting, embeddings, retrieval-augmented generation, parameter-efficient tuning, and multimodal evaluation.
• Experience with forecasting, temporal deep learning, or longitudinal modeling.
• Experience working with administrative, education, workforce, economic, health,…