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Fellowship in Medical Physics & Electrophysiology

Health-Physical & Computational Intelligence Laboratory

Dr. Konstantinos Koutsoyiannis is Professor of Medical Physics and Electrophysiology at the University of Patras and Director of the Health-Physical & Computational Intelligence Laboratory. He holds a degree in Physics from the University of Patras and a PhD in Medical Physics, with doctoral research focused on physico-mathematical signal analysis of acoustically evoked neurodynamic responses. He has over a decade of professional experience as a Clinical Medical Physicist in diagnostic centers, medical device companies, and vocational education leadership roles. His academic teaching spans undergraduate and postgraduate programs in medical informatics, biophysics, electrophysiology, artificial intelligence, biostatistics, non-ionizing radiation, and research methodology. Dr. Koutsoyiannis’ research interests include medical physics, biophysics, biosignal processing, electrophysiology, artificial intelligence, and online education, with numerous publications and conference contributions. He actively serves in European scientific bodies, including EFOMP committees and working groups, and the European Committee for the Evaluation of Medical Devices (ExPaMed).

Main Areas of Interest

 Physiological Measurements and Bioinstrumentation: Acquisition and interpretation of physiological signals including electrophysiological, cardiovascular, respiratory, and biophysical measurements using advanced medical and research-grade instrumentation.
 Biomedical Signal Processing and Feature Extraction: Preprocessing, filtering, time–frequency analysis, and extraction of meaningful physiological biomarkers from complex biological signals.
 Advanced Statistical Analysis of Physiological Data: Application of improved statistical methods for large-scale physiological datasets, including multivariate analysis, modeling of variability, and outcome prediction.
 Artificial Intelligence in Physiological Data Analysis: Use of machine learning and deep learning approaches for pattern recognition, classification, and predictive modeling of physiological outputs.
 Data Integration and Translational Interpretation: Integration of multimodal physiological measurements with computational analysis to support clinical decision-making and translational research applications.

Specialties

Technology Development

Techniques You Will Learn

 Electro-Diagnostic Techniques: Acquisition and interpretation of electrophysiological signals using advanced bioinstrumentation for diagnostic and monitoring purposes.
 Electro-Therapeutic Modalities: Hands-on training in therapeutic electrical stimulation techniques, safety standards, and parameter optimization.
 Electromagnetic Radiation Measurements: Measurement and assessment of electromagnetic fields and radiation in biomedical and clinical settings.
 Advanced Data Analysis and AI Applications: Processing and interpretation of electro-physiological data using improved statistical methods and artificial intelligence approaches.

Curriculum

Foundations in Physiological Measurements and Bioinstrumentation
·       Orientation to laboratory workflow, safety, ethics, and experimental planning.
·       Introduction to physiological measurements and biomedical sensors.
·       Hands-on training in electro-diagnostic signal acquisition and calibration.
·       Overview of physiological signal types (bioelectric, cardiovascular, respiratory) and measurement standards.
Electro-Diagnostic and Electromagnetic Measurement Techniques
·       Advanced training in electro-diagnostic methodologies and electrophysiological recordings.
·       Practical sessions on electromagnetic radiation and field measurements in biomedical environments.
·       Instrumentation setup, signal conditioning, and noise reduction techniques.
·       Case discussions on diagnostic interpretation and measurement accuracy.
Electro-Therapeutic Modalities and Applied Physiology
·       Hands-on exposure to electro-therapeutic techniques and stimulation protocols.
·       Parameter optimization, safety guidelines, and regulatory considerations.
·       Monitoring physiological responses to therapeutic interventions.
·       Clinical and research case studies integrating electro-therapy applications.
Advanced Data Analysis, Statistics, and Artificial Intelligence
·       Processing and feature extraction from physiological and electro-physiological datasets.
·       Application of improved statistical methods for physiological data interpretation.
·       Introduction to machine learning and AI-based models for pattern recognition and prediction.
·       Multidisciplinary research discussions, final presentations, feedback session, and certification of completion.

Program duration: 6 -Week Visiting Fellowship is the default program structure

Optional extension: Up to 12 weeks
Occasional short visit: 2 weeks (by arrangement)

Accommodation

Try airbnb.com

Fees and Program Logistics

This program has the following durations available:

Duration	Fee
6 weeks	$1,500.00

Who Can Apply

Biomedical scientists

Biophysicists

Biomedical engineers

Physiotherapists

Medical Doctors

Host Details

Host Name: Prof. Constantinos Koutsojannis

Affiliation: Health-Physical & Computational Intelligence Laboratory

Address: University of Patras Campus, Building B, 1st floor 26504 Rio

Website URL: www.upatras.gr

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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