Artificial Intelligence/Machine Learning Postdoctoral Fellowship

About the role

The Department of Defense (DoD) is offering a post-doctoral fellowship at the U.S. Army Medical Research and Development Command - Institute of Surgical Research (USAMRDC ISR) within the Expeditionary Medical Systems (EMS) Department. The fellowship involves engaging with data scientists and systems engineers in research projects focusing on advanced modeling techniques such as artificial intelligence (AI) and machine learning (ML).

Responsibilities

• Designing and implementing Discrete Event Simulation (DES) models to simulate complex system workflows, queuing networks, logistics, and operational state changes within the digital twin environment.
• Developing deep learning architectures for time-series forecasting, anomaly detection, and predictive maintenance of the physical asset.
• Designing and training Physics-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system.
• Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation environments.
• Utilizing Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models to augment sparse datasets and simulate edge-case operational and clinical scenarios.
• Designing, training, and fine-tuning computer vision models to extract clinically relevant information from medical imagery, video feeds, and operational sensor streams.
• Building and integrating multi-modal models that fuse vision, text, audio, physiological signals, and structured telemetry to support holistic situational awareness and decision support in combat casualty care.
• Adapting large vision-language models (VLMs) and multi-modal foundation models, including prompt engineering, fine-tuning, and retrieval-augmented approaches, to enable natural language interaction with visual and clinical data.
• Creating scalable annotation, preprocessing, and augmentation pipelines for image and video datasets, including handling of imbalanced, noisy, and low-resource medical data.
• Developing evaluation protocols for vision and multi-modal models, including bias assessment, robustness testing, explainability, and clinically meaningful performance metrics.
• Optimizing computer vision and multi-modal models for efficient, low-latency inference on resource-constrained or edge devices relevant to point-of-injury and field telemedicine environments.
• Implementing continuous, bi-directional data flow required to keep the digital twin synchronized with the physical state in near real-time.
• Connecting predictive ML models, computer vision systems, and simulations with visualization platforms to provide actionable insights to human operators.
• Implementing frameworks for continuous model monitoring, retraining, versioning, and deployment to ensure the digital twin and associated AI models adapt to degradation or changes in the physical system over time.

Requirements

You will hold a PhD in a STEM field listed in the opportunity's eligibility section. Your degree must have been received within five years of the appointment start date. You will also need a security clearance while participating in this program. Any offer made is considered tentative pending a favorable outcome of the investigation.

Qualifications

• The qualified candidate will hold a PhD in a STEM field listed in the opportunity's eligibility section.
• Your degree must have been received within five years of the appointment start date.

Preferred Skills

• Discrete Event Simulation for workflows/logistics
• Deep learning for time-series forecasting & anomaly detection
• Computer vision (detection, segmentation, classification, pose) using Vision Transformers & CNNs
• Multi-modal models fusing imagery, video, text, audio & physiological data
• VLMs, multi-modal foundation models, fine-tuning & prompt engineering
• Physics-Informed Neural Networks & hybrid models
• Deep Reinforcement Learning for process optimization
• GANs, VAEs, diffusion models for synthetic data & simulation
• Bayesian networks & uncertainty quantification
• ML frameworks (PyTorch, TensorFlow, Hugging Face, OpenCV) & Python
• Automated pipelines for multi-modal data
• Edge/real-time inference optimization (quantization, pruning, ONNX/TensorRT)
• Visualization platforms (e.g., NVIDIA Omniverse)
• MLOps (monitoring, retraining, deployment)
• Model evaluation, explainability & responsible AI

Pay

The stipend will be determined by USAISR and is typically based on a participant’s academic standing, discipline, experience, and research facility location.

Schedule

Appointments are initially for one year with the option to extend the appointment for up to four additional years, contingent upon project needs and funding availability.

Benefits

Health Insurance Supplement (Participants are eligible to purchase health insurance through ORISE)

Relocation Allowance

Training and Travel Allowance

About USAISR

The U.S. Army Institute of Surgical Research (USAISR) is one of six research laboratories within the U.S. Army Medical Research and Development Command of the U.S. Army Medicine Command (USAMRDC). The Institute is the Army's lead research laboratory for improving the care of combat casualties. The mission of the Institute is to "Optimize Combat Casualty Care."

About ORISE

This program, administered by Oak Ridge Associated Universities (ORAU) through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and DoD. Participants do not enter an employment relationship with ORISE, ORAU, DoD or any other office or agency. Instead, you will be affiliated with ORISE for the administration of the appointment through the ORISE appointment letter and Terms of Appointment. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE.

Application Requirements

• Zintellect Profile
• Educational and Employment History
• Essay Questions (goals, experiences, and skills relevant to the opportunity)
• Resume (PDF)
• Transcripts/Academic Records - Please upload a copy of a transcript for your current or most recent degree program that meets the disciplinary qualifications of the opportunity. Click here for detailed information about acceptable transcripts.
• One Recommendations