Ulrich Doll is a Tenure Track Assistant Professor at the Department of Mechanical and Production Engineering, Aarhus University's College of Engineering. His research focuses on experimental fluid mechanics, laser-optical flow diagnostics, and turbomachinery flows. Key expertise: Flow distortion measurement, turbulent combustion, and machine learning integration Primary affiliation: Fluids and Energy section Techniques used: Filtered Rayleigh Scattering (FRS), Laser-Induced Fluorescence (LIF), CFD validation Recent work demonstrates trends in hydrogen fuel combustion , non-intrusive aero-engine diagnostics , and machine learning-assisted flow analysis . Publications span fluid dynamics, gas turbine technology, and nuclear safety radiation modeling.
Haiyuan Wang is a Researcher at the Department of Energy Conversion and Storage at the Technical University of Denmark (DTU) . Their work focuses on atomic-scale modeling of materials for energy applications, particularly perovskite solar cells and quantum emitters in 2D materials. Research Interests Research spans Perovskite solar cell stability and defect passivation Quantum emitter engineering in 2D materials Machine learning for materials discovery Strain engineering in oxide membranes Electronic and vibronic coupling at interfaces Recent Publications Key contributions include 2025: Defect passivation in perovskite photovoltaics 2025: Room-temperature quantum emitters in α-MoO₃ 2024: Spatial conformation engineering for stable perovskite cells with methodologies ranging from first-principles simulations to data-driven approaches.
Pernille Klarskov Hansen is an Associate Professor at the Department of Electrical and Computer Engineering, Aarhus University, specializing in Electronics and Photonics. Her work bridges fundamental THz spectroscopy with industrial applications for sustainable materials. University: Aarhus University Department: Electrical and Computer Engineering Rank: Associate Professor Her research focuses on terahertz (THz) spectroscopy for material characterization, particularly in biodegradable polymers and flame retardants . She combines thermal-strain engineering with machine learning to enhance piezoelectric properties in eco-friendly materials. Key application areas include plastic waste sorting and environmental safety . Recent publications highlight her expertise in in-line sensing systems for industrial recycling, such as hyperspectral imaging of flame retardants in polyolefins. Her work spans journals like Nanoscale Horizons and conferences like FLEPS 2024, emphasizing scalable solutions for green electronics and marine pollution mitigation .
Kasper Mayntz Paasch is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark, specializing in the Centre for Industrial Electronics. Holding a PhD in Robotics, an MSc in Optics, and a BA in Science of Religion, his work bridges engineering innovation with historical analysis. PhD: Optimization of Multi-MW PV Power Plants (Mads Clausen Institute, 2016) MSc: Optics (Aalborg University, 1991) BA: Science of Religion (Aarhus University, 2008) His research spans Power Electronics and Photovoltaic Systems , with innovative applications of machine learning in component selection for power converters. Concurrently, he investigates Byzantine Numismatics and Cultural Heritage through analytical methods like X-Ray Fluorescence Spectroscopy. Recent publications highlight his dual expertise: Intelligent Inductor Characterization (2025) and Analysis of Byzantine Gold Coins (2024). Awards include recognition from Elektrofondet (2020) for engineering contributions. Scientific Awards Elektrofondet Award (2020) Active in EU and industry projects (e.g., Interreg SmartPowerConversion), Paasch also serves on committees like the Faculty of Engineering (since 2022) and contributes to media outreach on renewable energy advancements.
Alexander Dybdahl Rathcke is a Postdoctoral Researcher (Research Fellow) at the Department of Space Research and Technology, Technical University of Denmark (DTU Space), specializing in exoplanet atmospheric characterization using space-based observatories including Hubble and James Webb Space Telescopes. His work focuses on developing advanced data analysis techniques to interpret transmission spectra and correct for stellar contamination. Education: PhD in Exoplanetary Atmospheres, Technical University of Denmark (2019-2022) Rathcke's research spans terrestrial exoplanets, hot Jupiters, and sub-Neptune atmospheres, with expertise in transmission spectroscopy, stellar activity mitigation, and machine learning applications. He actively develops computational frameworks like TAU for telluric correction and contributes to major observational campaigns such as the TRAPPIST-1 JWST Community Initiative. His fingerprint reveals deep specialization in planetary atmospheres (90%), transmission spectra (81%), hot Jupiters (71%), and space telescope instrumentation. Recent publications demonstrate a clear trajectory toward characterizing Earth-like exoplanets using JWST, with increasing emphasis on stellar contamination correction and neural network-based data processing. His work bridges observational astronomy, atmospheric physics, and computational methods to advance exoplanet science. Scientific Awards: No awards documented in available sources Dr. Rathcke currently supervises PhD candidate K. E. Plainos on atmospheric characterization of terrestrial and sub-Neptune exoplanets (2025-2028), building on his own doctoral work in exoplanetary atmospheres. His projects have generated significant academic impact, with multiple publications featured in high-profile journals and cited across 7+ news outlets, 10+ social media platforms, and referenced in Wikipedia. He operates within DTU Space's Astrophysics and Atmospheric Physics group, maintaining extensive international collaborations across Europe and North America as evidenced by multi-institutional publications. His research directly contributes to UN Sustainable Development Goals through planetary climate science and advanced observational techniques.
Esmaeil Nadimi is a Professor and Head of Unit at The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, where he leads the Applied AI and Data Science research unit. He holds multiple concurrent positions including Visiting Professor at Harvard University, Chair of Data Analytics at SDU eScience Center, and research roles with Innovationsfonden and Swiss National Science Foundation (SNSF) in Medicine & Biology and Health & Well-being domains. His educational background includes a Ph.D. in Electrical and Computer Engineering from Aalborg University (2008) and an M.Sc. in Electrical Engineering (Control Theory) from Sharif University of Technology (2004). Research focuses on foundational AI methodologies including Physics-Infused Learning, Meta & Transfer Learning, with applied research in healthcare (medical imaging, cancer detection, diabetic neuropathy), energy systems, and optical technologies. Core expertise spans machine learning, computer vision, statistical modeling, and medical AI systems development. Publications demonstrate strong emphasis on AI applications in medical diagnostics, particularly capsule endoscopy, cervical/colorectal cancer detection, and diabetic complication screening. Recent work increasingly addresses explainable AI frameworks and practical implementation pathways for clinical adoption. Leadership includes supervision of industrial PhD/postdoc programs and development of academic courses in AI/Data Science. Current grants involve Innovationsfonden and SNSF-funded projects. Leads multiple research teams focused on medical AI systems at SDU Applied AI and Data Science unit.
Christian Janfelt is an Associate Professor in the Department of Pharmacy at the University of Copenhagen's Faculty of Health and Medical Sciences. His research focuses on mass spectrometry imaging (MSI) techniques, particularly Desorption Electrospray Ionization (DESI) and Matrix Assisted Laser Desorption Ionization (MALDI)-MSI, with applications in pharmaceutical analysis, drug delivery studies, and plant science. Education: Ph.D. in Analytical Chemistry, Department of Chemistry, University of Copenhagen (2008) Master of Physics and Chemistry, University of Southern Denmark, Odense (2005) Bachelor of Chemistry, University of Southern Denmark, Odense (2002) Dr. Janfelt's research centers on instrumental development of mass spectrometry for pharmaceutical applications. His work specifically involves imaging chemical compounds in biological samples using advanced MSI techniques. He utilizes specialized MALDI instrumentation with sophisticated laser optics and Orbitrap mass spectrometry for high-resolution imaging of biological samples, enabling detailed identification of chemical compounds. His current research spans multiple domains including drug distribution studies in laboratory animals, drug delivery across biological membranes, and analysis of natural products in plant materials. His recent publications demonstrate strong activity at the intersection of analytical chemistry, dermatology, and pharmacology, with particular emphasis on cancer diagnostics through MALDI-MSI combined with machine learning approaches. His work shows consistent collaboration with medical researchers, particularly in skin cancer diagnostics and treatment. Dr. Janfelt teaches courses in instrumental analytical chemistry, pharmaceutical analytical chemistry, and mass spectrometry. His teaching portfolio includes laboratory exercises in introductory chemistry, inorganic chemistry, and instrumental analytical chemistry, along with lectures on mass spectrometry. With fluency in Danish (mother tongue), English, German, and French, Dr. Janfelt maintains international collaborations, having previously worked with research groups at Justus-Liebig Universität Giessen in Germany and Purdue University in the USA.
