Simulation and Design
Ph.D.

Focus: Health Sciences

There has been a growing reliance on the use of simulation and design in all areas of science and technology thanks to powerful computational tools developed in recent years and the blurring lines between disciplinary areas, specifically between architecture, urban design, engineering, health sciences, business, and all areas of science and technology. As a technological university, LTU realizes the importance of readying professionals for this emergence and is proud to announce an innovative and strongly interdisciplinary Ph.D. program in Simulation and Design.

LTU, with its five colleges of Architecture and Design, Arts and Sciences, Business and Information Technology, Engineering, and Health Sciences and the varied expertise of its highly qualified faculty, is uniquely positioned work with Ph.D. students to pioneer advancements in simulation and simulation-driven design in all academic disciplines and foster interdisciplinary research that drives innovation and creates transformative solutions to global challenges.

The vast variety of interdisciplinary applications of simulation and design underscores the opportunities for students interested in an intensive research-focused Ph.D. program that equips graduates in and across all academic disciplines with the skills to harness simulation and design to lead the next wave of technological advancements for solutions to the world’s challenges.

In the health sciences, despite the level of complexity involved, simulation has had transformative implications in many areas. Medical professionals now rely on sophisticated simulations to train for complex procedures, enhance diagnostic accuracy, and improve patient outcomes. Virtual reality (VR) and augmented reality (AR) technologies create immersive training environments for surgeons, allowing them to practice techniques in a risk-free setting. Furthermore, biomedical simulations enable the study of physiological processes at a molecular level, contributing to advancements in drug development and personalized medicine. By modeling the human body’s responses to treatments, researchers can predict the efficacy and potential side effects of new therapies, accelerating the path from laboratory to clinical use. Reverse engineering the human brain has been identified by the National Academies of Engineering as one of the 21st Century’s fourteen grand challenges and has provided the genesis of artificial intelligence. As Generative AI models become more advanced, we can expect even faster and more accurate drug discovery processes. These models could result in:

1. Improved Patient Outcomes
   • With the ability to create highly personalized treatment plans and tailor therapies to individual patients, Generative AI could significantly improve patient outcomes. Treatments could be adjusted in real-time based on patient responses, leading to more effective and less invasive care.
2. Ethical and Regulatory Considerations
   • As we move forward, it’s crucial to address the ethical and regulatory implications of Generative AI in healthcare. Ensuring data privacy, transparency in AI-generated recommendations, and addressing biases in AI models will be essential for the responsible deployment of this technology.

» Potential Research Areas

• Clinical Simulation:
  • Development and application of clinical simulations for training and research
  • Simulation-based medical education and competency assessment
  • Medical device simulations
  • Disease and treatment modeling
• Health Systems Simulation:
  • Simulation of healthcare systems and processes
  • Optimization of healthcare delivery and resource management
• Public Health Simulation:
  • Modeling the spread of infectious diseases (epidemiology)
  • Simulation of public health interventions and policies
• Generative AI
  • Advance predictive analytics by generating simulations of disease progression based on patient data improving patient outcomes.
  • Predict not only the efficacy of new drugs but also their potential long-term effects and interactions with other treatments as well as lead to earlier detection of diseases and proactive interventions.