Computer-Aided Design Professors and Supervisors in Norway

Andrea RaffoView profile

Researcher

University of OsloNorway

Andrea Raffo serves as a Researcher in the Department of Molecular Biosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences, affiliated with the Computational 3D Genomics research group led by Paulsen. His work bridges computational mathematics and biological applications through advanced geometric analysis techniques. His educational background includes a PhD in Mathematics from the University of Oslo, preceded by Bachelor's and Master's degrees in Mathematics from the University of Genoa (Italy). This strong mathematical foundation enables his specialized research at the intersection of computational geometry and life sciences. Research Focus: Development of computational methods for geometric pattern recognition in biological systems Core Methodologies: 3D point cloud analysis, shape characterization, and algorithmic pattern detection Application Domains: Chromosome conformation capture data analysis, protein channel dynamics, and super-resolution microscopy image processing His publication record demonstrates consistent contributions to computational geometry with direct applications in biosciences. Recent work shows increasing specialization in translating geometric pattern recognition techniques to solve concrete problems in genomics and structural biology, particularly through participation in international benchmarking initiatives like SHREC. While no formal awards are documented in current records, his research has been consistently published in high-impact venues including Computer-Aided Design, Computer Aided Geometric Design, and Frontiers in Molecular Biosciences. His collaborative approach is evident through extensive co-authorship networks across European and Asian research institutions. Raffo actively contributes to the Computational 3D Genomics group, focusing on developing analytical tools for chromosome architecture analysis and protein structure characterization. His current projects involve advanced segmentation techniques for HP1α condensate structures in super-resolution microscopy, representing the cutting edge of computational bioscience methodology development.

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Hans OlofsenView profile

Senior Lecturer

UiT The Arctic University of NorwayNorway

Hans Olofsen serves as a Senior Lecturer and Study Program Manager for Bachelor Computer Engineering and Computer Engineering - Further Education at the Department of Computer Science and Computational Engineering, UiT The Arctic University of Norway, located at the Narvik campus. His professional activities span both academic instruction and research leadership within computational engineering disciplines. Dr. Olofsen's research interests center on Medical Image Analysis with particular focus on Alzheimer's disease biomarkers, Computational Engineering applications in healthcare, and Artificial Intelligence solutions for medical image processing. His technical expertise includes developing algorithms for ventricular shape analysis, creating autonomous virtual robots for 3D medical image navigation, and designing wearable technology systems for fall risk assessment in elderly populations. Analysis of his publication record reveals a consistent research trajectory in computational medical imaging spanning over 15 years, with significant contributions in ventricular shape biomarkers for Alzheimer's disease, 3D medical image exploration systems, and wearable technology applications in geriatric care. His work demonstrates interdisciplinary collaboration across neuroscience, computer science, and biomedical engineering domains. As a member of the Simuleringer (Simulations) research group at UiT, Dr. Olofsen contributes to advancing computational methods in medical applications with a specialization in Artificial Intelligence techniques. His office is located in Campus Narvik D2210, where he manages both his teaching responsibilities for computer engineering programs and his research activities.

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Heinz Adolf PreisigView profile

Professor

Norwegian University of Science And TechnologyNorway

Heinz Adolf Preisig is a Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). With an extensive international academic career, Professor Preisig has held positions at Texas A&M University (USA) as Associate Professor, University of New South Wales (Australia) as Senior Lecturer, and Eindhoven University of Technology (Netherlands) as Professor of Chemical Engineering, Physics, and Electrical Engineering before joining NTNU. His research focuses on Process Systems Engineering with particular emphasis on ontology-based computational engineering, multi-scale modeling, and materials modeling frameworks. Professor Preisig has developed systematic approaches for constructing, manipulating, and simplifying process models through hierarchical ontology-based software suites. His work bridges theoretical modeling frameworks with practical industrial applications, with significant contributions to process modeling methodologies that integrate mechanistic reductionism with holistic empirical components. His research spans from fundamental theoretical developments to applied industrial projects across multiple sectors including energy, materials, and chemical processing. Analysis of Professor Preisig's publication record (2017-2022) reveals a clear trajectory toward more sophisticated ontology-based modeling approaches with increasing integration of semantic web technologies with traditional process engineering. His work demonstrates consistent development of frameworks that enable better model interoperability, reusability, and systematic construction across different domains. A significant portion of his recent work focuses on multi-scale modeling applied to complex materials like polyurethane foams and biomass conversion processes, showing his ability to connect fundamental physical principles with practical engineering applications through advanced computational frameworks. Professor Preisig has been instrumental in major European research initiatives: VIPCOAT Horizon2020 Project: Creating an open innovation platform for corrosion protection coatings in the aeronautic industry MarketPlace EC Horizon 2020 project: Developing a Materials Modelling and Collaboration platform as a one-stop-shop for materials modeling MoDeNa Framework 7 Project: Building multi-scale software frameworks for polyurethane foam modeling EcoLodge: Bio fuel production project using coupled bio reactors He teaches core courses at NTNU including TKP4106 Process Modelling, TKP4135 Process Systems Engineering, and supervises chemical engineering specialization projects. His teaching philosophy centers around models as the foundational concept in engineering education, emphasizing multi-scale understanding and systematic model construction. Professor Preisig leads the Process Systems Engineering group at NTNU, which develops computer-aided modeling tools for systematic process model construction, documentation generation, and code implementation across various simulation environments.

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Morten BrekkeView profile

Professor

University of AgderNorway

Morten Brekke is a Professor at the Department of Engineering Sciences , University of Agder (UiA). He served as Vice-Rector for Education (2019–2023), leading the strategic initiative "Learning and Education for the Future". His academic background includes a Cand. Scient in Theoretical Astrophysics. He is a central figure in MatRIC (2014–2024), an international mathematics education center. His work focuses on innovative teaching methods, particularly Computer Aided Assessment (CAA) and video-based flipped classrooms using tools like STACK and Panopto. Key roles include: Chair of EUA Learning & Teaching Thematic Peer Group (2023–2024) Head of the Council for Quality Enhancement in Norway (2021–2023) Member of the Skills and Competences Mission Board (FORTHEM Alliance) Editorial board member of TEAMAT (Oxford University Press) Research interests emphasize: CAA integration in mathematics education Video-driven teaching strategies Assessment innovation for engineering students Recent achievements include the 2018 Excellent Teaching Practitioner award (UiA’s first recipient). His work has reduced engineering student failure rates via STACK-based projects and improved calculus performance through blended learning approaches.

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Nishamol KuriakoseView profile

Research Fellow

University of OsloNorway

Nishamol Kuriakose is a Research Fellow at the Fukui Institute for Fundamental Chemistry, Kyoto University, Japan. Previously, they held positions including Post-doctoral Research Fellow at The Hebrew University of Jerusalem (Israel), Research Associate at IISER Pune (India), and Research Scientist at A*STAR-IHPC (Singapore). Their research focuses on DFT studies, small molecule activation, catalysis, and machine learning applications in chemistry. Education: Ph.D. in Chemical Science, CSIR-National Chemical Laboratory, Pune, India M.Sc. Chemistry, University of Kerala, India (Mar Ivanios College) B.Sc. Chemistry, Mahatma Gandhi University, India (Assumption College) Research Interests: Exploring computational and experimental methods to advance catalytic processes and material science. Key areas include transition metal and main group catalysts, surface chemistry of MXenes, and drug-target interactions through molecular modeling. Machine learning aids in optimizing reaction pathways and predicting catalytic behaviors. Awards: PBC “Study in Israel” Fellowship H2020-MSCA-IF-2020 - Seal of Excellence Shell India Computational Talent Prize Advising & Grants: While no formal advisees are listed, their work has been supported by grants including the H2020-MSCA fellowship. Active collaborations involve theoretical chemistry groups exploring catalytic mechanisms and material surfaces. Labs/Teams: Member of the Theoretical Chemistry research group, focusing on computational modeling of catalytic systems and environmental chemistry applications.

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Ekaterina KimView profile

Associate Professor

Norwegian University of Science And TechnologyNorway

Ekaterina Kim is an Associate Professor at the Department of Marine Technology, Norwegian University of Science and Technology (NTNU), within the Faculty of Engineering. She specializes in Arctic engineering, focusing on ice mechanics, structural analysis, and AI-driven solutions for extreme environments. Her work bridges fundamental material science principles with engineering applications, addressing challenges such as ice-structure interactions, risk management in ice-covered waters, and autonomous systems for sea ice monitoring. Education: Formal engineering training at Peter the Great Polytechnic University (St. Petersburg), followed by a PhD at NTNU (2010–2017). Her research integrates interdisciplinary approaches, including machine learning for sea ice classification, probabilistic risk models, and numerical simulations of ice dynamics. Research Interests: Arctic technology and digitalization AI applications in extreme environments Risk analysis for ice navigation Sea ice parametrization and segmentation Structural analysis of icebreakers Publications: Over 30 peer-reviewed articles, focusing on Bayesian networks for risk modeling, autonomous sea ice mapping systems, and material models for ice impact. Recent work emphasizes AI-driven solutions for operational safety and data-driven decision support in the Arctic. Awards & Recognition: While no specific awards are listed, her contributions to Arctic research are highlighted through collaborative projects like the Arctic Hive Mind Group and the AIDing Arctic initiative. She also contributes to education, teaching courses on finite element methods and ice-structure interaction. Labs & Teams: Leads the Arctic Hive Mind Group at NTNU, fostering interdisciplinary collaboration between engineers, data scientists, and Arctic experts. Involved in the Risk Group within the Department of Marine Technology.

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Harald SkulbergView profile

Associate Professor

Oslo School of Architecture and DesignNorway

Harald Skulberg serves as an Associate Professor in the Department of Design at Oslo School of Architecture and Design (AHO), where he has established himself as a dedicated educator and researcher specializing in design pedagogy and product design methodology. His academic profile demonstrates consistent scholarly contributions to design education conferences and proceedings over nearly two decades, with particular focus on how students approach design problems and develop creative solutions. Skulberg's research interests primarily center on the cognitive aspects of design thinking, with special emphasis on mental scaling techniques, light manipulation in 3D form exploration, and cross-disciplinary approaches to complex design projects. His work examines how students navigate the design process, particularly during diploma-level work, and investigates how various media stimuli and digital tools can enhance creative potential in design education. He has made significant contributions to understanding how the pandemic context transformed design education practices, documenting the shift to virtual classrooms and the resulting pedagogical adaptations. Analysis of Skulberg's publications reveals a consistent research trajectory focused on the intersection of cognitive psychology and design education. His work demonstrates how mental scaling techniques serve as crucial tools for designers to conceptualize and manipulate form, particularly when working with light as a design element. More recently, his research has addressed the challenges and opportunities presented by remote design education, documenting how design pedagogy adapted during the pandemic era. His publications appear consistently in major design education conference proceedings, particularly the International Conference on Engineering and Product Design Education (E&PDE). Skulberg has also contributed to several collective works documenting student projects at AHO, including the 'AHO WORKS Studies' publications that showcase design education outcomes. His work bridges theoretical understanding of design cognition with practical applications in the design studio environment, making him a significant contributor to contemporary design education scholarship.

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Eva RaulsView profile

Professor

University of StavangerNorway

Eva Rauls is a Professor of Materials Physics in the Department of Mathematics and Physics at the University of Stavanger, under the Faculty of Science and Technology. Her research focuses on computational investigations of molecular adsorbates, catalytic properties, and electronic behavior of molecules on surfaces such as metals and graphitic materials. Her research interests include computational materials science, surface physics, supramolecular materials, and applications in CO2 capture and gas separation. She employs first-principles and semi-empirical methods, particularly density functional theory (DFT), to model and predict the behavior of functional materials. Current projects involve the design of supramolecular materials and optimization of metal-organic frameworks (MOFs) for environmental applications. The recent publication trends highlight a strong focus on pillararenes for CO2 adsorption, with detailed DFT studies analyzing functional group effects, cavity size, and molecular interactions. These works span chemistry, materials science, and environmental engineering, demonstrating interdisciplinary impact. Scientific awards include: Feodor-Lynen Fellowship of the Alexander-von-Humboldt-Society (2004–2006) Eva Rauls advises students such as Ho Quoc Duy and collaborates closely with the chemistry department and researchers like Uwe Gerstmann and Christoph Tegenkamp. Her work is supported by active research projects in functional material design for sustainable technologies. She regularly presents at international conferences including ECOSS, VCTP, and European Graphene Forum. She leads research efforts in atomistic modeling and computational studies of 2D heterostructures and molecular systems, indicating an active research group focused on theoretical and computational materials physics.

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Pål HalvorsenView profile

Professor

Oslo Metropolitan UniversityNorway

Pål Halvorsen is a Professor at the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. He works at the intersection of computer science and applied domains, with a particular focus on multimedia systems, distributed computing, and healthcare applications. Specializes in distributed multimedia systems Active in AI-driven forensic psychology applications Conducts research on medical imaging and diagnostics Develops sports analytics datasets and tools Works on communication and distributed systems His research spans several key areas of computer science, particularly focusing on multimedia systems and their applications in healthcare, sports analytics, and forensic psychology. He leads projects involving AI-driven child avatars for investigative interview training, develops datasets for medical and sports applications, and explores innovative approaches to image analysis and time-series data processing. Recent publications demonstrate strong activity in applying computer vision and deep learning to medical diagnostics, particularly in gastrointestinal tract analysis and ophthalmology. His work on sports analytics includes creating comprehensive datasets for ice hockey and soccer, while his forensic psychology research focuses on AI-enhanced interview training for child abuse investigations. Halvorsen collaborates extensively across disciplines, working with researchers in psychology, medicine, and sports science. His projects often involve developing novel tools for data analysis, including approaches to multimodal data handling, visual deep learning verification, and AI-enhanced prompt generation techniques.

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Andreas KotsadamView profile

Research Fellow

University of OsloNorway

Andreas Kotsadam is a Senior Research Fellow at the Frisch Centre, affiliated with the University of Oslo. His research focuses on development economics, labor markets, gender studies, and political economy, with extensive fieldwork in Sub-Saharan Africa and Scandinavia. He employs experimental methods and large-scale data analysis to examine social policy interventions, economic behavior, and institutional impacts. Research Interests: Kotsadam's work spans multiple domains including the economic determinants of gender-based violence, effects of employment programs on political participation, wealth redistribution impacts, and ethnic integration. His research utilizes field experiments to test causal relationships in real-world settings, particularly focusing on policy-relevant outcomes in developing economies. Research Projects: Current projects include studies on educational transitions, ethnic segregation, retirement decisions, and welfare reform. He collaborates on Norwegian Fiscal Studies research examining tax policies and labor market interventions. Recent work includes designing field experiments for the Norwegian government's pilot scheme on youth employment incentives. Methodological Approach: Kotsadam's publications demonstrate a strong focus on experimental and quasi-experimental designs, with frequent use of randomized controlled trials, natural experiments, and large-scale administrative data analysis across diverse geographical contexts.

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