Dr. Mikkel Seyer Hansen

Associate Professor

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Biography

Mikkel Seyer-Hansen is a Clinical Associate Professor at Aarhus University, where he specializes in surgical training and medical education. He has been actively involved in research and projects focused on enhancing the quality of learning environments for medical students and professionals. Over the years, Seyer-Hansen has contributed to numerous research outputs, with a particular emphasis on self-regulated learning and innovative educational strategies within the clinical setting. He has collaborated on various projects aimed at talent development in medicine, drawing parallels to elite sports training methodologies. His expertise extends to guiding medical education through research-informed practices, which emphasizes excellence in clinical competencies and the preparation of future healthcare professionals. Seyer-Hansen is also recognized for his contributions to enhancing postgraduate surgical training, facilitating collaborations that aim to integrate self-directed learning into clinical practice. Throughout his career, he has delivered significant contributions to the academic community, participating in numerous research outputs each year and actively engaging in collaborative projects.

Research Interests

Experience

Clinical Associate Professor

— Present

Aarhus University • Aarhus, Denmark

Involved in teaching and research related to clinical practice and medical education.

Awards

#

Research Abstract Award

Requirements for Aarhus University

Master Program
Requirements
GPA Requirement
Required:3
IELTS
Overall
Required:6.5
TOEFL
Total
Required:83
Prerequisites
Bachelor's degree in a relevant field Specific credit requirements in programming, algorithms, and computer systems
Application Checklist
  • Official Bachelor's degree certificate
  • Official transcripts of records
  • Course descriptions
  • Curriculum Vitae (CV)
  • Documentation of English language proficiency
Specialization Notes

Department of Computer Science offers tracks in Software Efficiency, Cryptography, and Data Science.