جست‌وجوی استاد و پژوهشگر

• Combustion Engineering
• Alternative Fuels
• Hydrogen Energy
• +۵ مورد دیگر

Terese Løvås serves as Vice Dean of Research and Innovation at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU), where she leads strategic development of research and innovation activities. She concurrently holds the position of Professor of Combustion and Thermodynamics within the Department of Energy and Process Engineering. Her leadership responsibilities include oversight of Centers of Excellence, Horizon Europe projects, and PhD researcher training. Her research focuses on combustion engineering and alternative fuel technologies , particularly investigating ammonia and hydrogen combustion for zero-emission engines, biomass gasification processes, and reactive multiphase flow modeling. She heads the Engine Lab at NTNU and teaches Thermodynamics, Heat, and Combustion courses. Her work bridges theoretical modeling with experimental validation in sustainable energy systems. Løvås actively contributes to major research initiatives including LowEmission (SFI center), ACTIVATE (ammonia-powered agricultural vehicles), AMAZE (ammonia zero-emission), and CAHEMA (marine ammonia/hydrogen engines). Her publications reveal strong trends in ammonia combustion chemistry , emissions reduction , and advanced computational modeling for sustainable fuel systems, with increasing focus on nitrogen oxide formation mechanisms and dual-fuel strategies. Member of the Board of Directors, Combustion Institute (2022–present) Joint Editor, Proceedings of the Combustion Institute (2019–present) Alumni Fellow in Engineering, Churchill College, Cambridge University As Vice Dean, she manages NTNU's Research and Innovation Committee and represents the faculty in NTNU's Research and Innovation Committee. She supervises multiple PhD candidates and leads international collaborations through projects funded by the Norwegian Research Council, Nordic Energy Research, and EU programs. Her laboratory work focuses on optical engine diagnostics and advanced combustion testing. Løvås maintains active industry engagement through her leadership in the ComKin Research Group and membership in the Institute of Physics and Scandinavian-Nordic Section of the Combustion Institute. Her current work emphasizes practical implementation of ammonia-fueled engine technologies for marine and agricultural applications.

• Computational Physiology
• Cardiac Electrophysiology
• Atrial Fibrillation
• +۷ مورد دیگر

Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .

• Power cycles and turbomachinery
• CO2 capture
• Process modeling and simulation
• +۳ مورد دیگر

Lars O. Nord is a Professor in the Department of Energy and Process Engineering at NTNU, specializing in thermal energy systems, CO2 capture technologies, and dynamic process modeling. He holds a PhD from NTNU (2010) and a Master's from Virginia Tech (2001). His research focuses on power cycles, turbomachinery optimization, and decarbonization strategies for energy systems. Nord has led projects such as DEXPAND and InnCapPlant, addressing CO2 capture under variable loads and expander efficiency in renewable systems. Current roles: Head of the Thermal Energy research group and teaches courses like Engineering Thermodynamics. Research highlights include thermal energy storage integration, moving bed adsorption processes, and offshore hybrid energy systems. His work spans over 80 publications, emphasizing CO2 capture dynamics, turbine design, and control strategies for flexible power plants. Notable collaborations include SINTEF and Aker Solutions. Nord advises multiple PhD candidates and has mentored alumni now leading roles in industry and academia.

Behzad Alaeiمشاهده پروفایل

دانشیار

• Structural Geology
• Sedimentary Basin Analysis
• Fault Zone Architecture
• +۶ مورد دیگر

Behzad Alaei serves as an Associate Professor in the Section for Study of Sedimentary Basins within the Department of Geosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences. His office is located in room K38 of the Geology Building at Sem Sælands vei 1, 0371 Oslo, with a professional email contact at behzad.alaei@geo.uio.no. Dr. Alaei maintains an active research profile with publications spanning from 2005 to the present, demonstrating his ongoing contributions to geological sciences. Dr. Alaei's research spans multiple critical areas within structural geology and sedimentary basin analysis, with particular expertise in fault zone architecture, seismic interpretation techniques, and CO2 storage site assessment. His work bridges theoretical geological concepts with practical applications in petroleum geology and carbon sequestration. A significant portion of his research focuses on the Norwegian Barents Sea region, where he has conducted extensive studies on normal fault systems and their geometric characteristics. His recent work increasingly integrates machine learning and deep learning approaches with traditional geological analysis, reflecting the evolving nature of geoscience research methodology. The analysis of Dr. Alaei's publication record from 2018-2024 reveals a strong thematic continuity in fault characterization research, with progressive incorporation of advanced computational methods. Early publications focused primarily on traditional structural analysis of fault systems in sedimentary basins, while more recent work demonstrates increasing integration of machine learning techniques for fault detection and characterization. A notable trend is the application of these geological insights to practical challenges in carbon capture and storage, particularly regarding fault risk assessment for CO2 storage sites in the North Sea region. His collaborative work with Anita Torabi appears consistently throughout this period, suggesting a strong research partnership. Dr. Alaei maintains an active research program with multiple ongoing projects related to sedimentary basin analysis and fault characterization. His work appears to involve significant collaboration with both academic and industry partners, particularly in the context of CO2 storage research. While specific grant details aren't provided in the available information, his consistent publication record across multiple high-impact journals suggests successful funding of his research activities over the past two decades.

• Numerical Analysis
• Differential Equations
• Geometric Numerical Integration
• +۵ مورد دیگر

Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.

John Rognesمشاهده پروفایل

استاد

• Algebraic Topology
• Algebraic K-Theory
• Geometric Topology
• +۳ مورد دیگر

John Rognes is a Professor at the Department of Mathematics , University of Oslo, specializing in Algebraic Topology, Algebraic K-Theory, and Geometric Topology. His research bridges number theory and homotopy theory, with a focus on structured ring spectra and topological modular forms. Education : International Baccalaureate (1984), Cand. Mag. in Mathematics (1985), Princeton MA (1987), and PhD (1990) under Gunnar Carlsson. Positions : Professor at UiO since 1998, Visiting roles at Stanford (1996), Chicago (1996), and Bonn (2005-2006). His research areas include Algebraic K-Theory , Stable Homotopy Theory , Topological Cyclic Homology , and Motivic Homotopy . Articles highlight work on Adams spectral sequences, redshift phenomena, Segal conjectures, and topological Hochschild homology of modular forms. Scientific awards include the 1999 Professor Ingerid Dal and Ulrikke Greve Dals prize, Fulbright-Hays Fellowship, and multiple grants from the Research Council of Norway (YFF, SUPREMA). He supervised 18 Master’s and 9 PhD students, including Paul Arne Østvær, Vigleik Angeltveit, and Alice Hedenlund. Leadership : Chairman of the Abel Committee (2014-2018), Program Leader for Master programs in Mathematics (2021-2024), and organizer of international symposia. Grants : YFF program 'Brave new rings' (7.1 MNOK), RCN projects on topology and motivic homotopy (total >30 MNOK).

Alexander Müller-Hermesمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Linear Algebra
• Functional Analysis
• +۱ مورد دیگر

Alexander Müller-Hermes is an Associate Professor at the Department of Mathematics, University of Oslo. His research focuses on quantum information theory with emphasis on mathematical questions in quantum Shannon theory and entanglement. He also explores functional analysis and convex geometry inspired by quantum phenomena. Before joining UiO, he held a Marie Skłodowska-Curie fellowship at University Claude Bernard Lyon 1 and was a postdoc at the Centre for Mathematics in Quantum Theory (QMATH), University of Copenhagen. He earned his PhD in Mathematics from Technical University Munich in 2015. Teaching includes advanced courses like Quantum Information Theory (MAT4430) and Linear Algebra (MAT1120). His research interests span quantum communication, entanglement theory, operator algebras, and functional analysis, with over 20 peer-reviewed publications since 2014. His work on fault-tolerant quantum coding and entanglement monotones has advanced theoretical foundations in quantum information. He collaborates with the Operator Algebras research group at UiO and is part of the QOMBINE project on quantum computation and many-body theory.

• Metal Forming
• Aluminum Alloys
• Product Development
• +۳ مورد دیگر

Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.

Lilya Budaghyanمشاهده پروفایل

استاد

• Boolean Functions
• Cryptography
• Information Security
• +۳ مورد دیگر

Lilya Budaghyan is a Professor at the Department of Informatics, University of Bergen, Norway, leading the Boolean functions research team at the Selmer Center. She holds a PhD from the University of Magdeburg (2005) and a habilitation from Paris 8 University (2013). Her research focuses on Boolean functions, particularly APN (Almost Perfect Nonlinear) functions and their cryptographic applications, with contributions to hardware implementations, equivalence relations, and cryptographic protocol design. She has authored the book *Construction and Analysis of Cryptographic Functions* and received awards including the TMS Starting Grant (2016) and Emil Artin Junior Prize (2011). Her work includes projects like the EU-funded BoolTI (2021–2025) and collaborations across institutions in Armenia, Germany, Italy, and France. Key research themes include constructing APN functions, analyzing their properties, and optimizing their hardware implementations for secure systems. Her recent publications emphasize hardware architectures for APN permutations, equivalence between cryptographic functions, and theoretical advancements in algebraic coding and finite fields. She serves as an editor for journals in cryptography and has organized international workshops such as WCC 2013.

• Machine Learning
• Artificial Intelligence
• Medical Imaging
• +۴ مورد دیگر

Elisabeth Wetzer is an Associate Professor in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research bridges artificial intelligence with healthcare applications, focusing on multimodal image registration, bias mitigation in AI, and physics-informed learning models. Current Role: Associate Professor, Machine Learning Group Research Themes: AI ethics, medical imaging, cross-modal representations, algorithmic fairness Her recent work explores technical challenges in PET imaging analysis and societal implications of AI bias. Collaborative projects span medicine, mathematics, and computer science disciplines. Key scientific contributions include: Physics-informed deep learning for PET image data Studies on multi-task learning efficacy in medical classification Research on gender bias in algorithmic systems She actively participates in diversity initiatives and public outreach, including presentations at Nobel laureate conferences and media engagements on AI ethics.

• Analysis
• Analytic Number Theory
• Functional Analysis
• +۳ مورد دیگر

Kristian Seip is a Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), since 1993. He has held leadership roles, including Department Chair (1997–2001) and Vice Dean for Education at the Faculty of Information Technology, Mathematics and Electrical Engineering (2006–2012). He holds an MSc and PhD from NTNU. His research focuses on analysis and analytic number theory, with notable contributions to Dirichlet series, Hardy spaces, and operator theory. Key awards include the Frontiers of Science Award (2024), Doctor Honoris Causa from Universitat de Barcelona (2024), and Inaugural Fellow of the American Mathematical Society (2012). He is a member of prestigious academies like the Norwegian Academy of Science and Letters and the Royal Norwegian Society of Sciences and Letters. Seip has led major research projects, including the 2012–2013 Centre for Advanced Study program on Operator-Related Function Theory (co-leader with Yurii Lyubarskii) and multiple grants from the Research Council of Norway. His work emphasizes Fourier methods, multiplicative analysis, and interpolation theory.

Hans Jonas Fossum Moenمشاهده پروفایل

دانشیار

• Autonomous Systems
• Sensor Technologies
• Robotics
• +۳ مورد دیگر

Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.

• Homotopy Theory
• Higher Category Theory
• Derived Algebraic Geometry
• +۳ مورد دیگر

Rune Haugseng is a Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), specializing in Homotopy Theory and Higher Category Theory. His research also intersects with Derived Algebraic Geometry and Topological Quantum Field Theories. Fields of Interest: Homotopy Theory, Higher Category Theory, Derived Geometry, and Quantum Field Theories. His recent publications focus on advanced topics in higher algebra, such as lax monoidal adjunctions, bispans, and the interplay between ∞-operads and symmetric monoidal structures. He has supervised multiple PhD and Master’s students, including Louis Martini and Klaus Caning, with projects ranging from internal categories in ∞-topoi to state-sum constructions of TQFTs.

• Algebraic Geometry
• Tropical Geometry
• Combinatorics
• +۳ مورد دیگر

Kris Shaw is a Professor in the Department of Mathematics at the University of Oslo, specializing in Algebra, Geometry and Topology. She leads the research group on Algebraic and Topological Cycles in Tropical and Complex Geometries, funded by the BFS. Shaw's research focuses on the connections between combinatorics and algebraic geometry over the real and complex numbers, with particular emphasis on tropical geometry, matroid theory, and real algebraic geometry. Her work bridges abstract mathematical theory with concrete geometric structures, exploring how combinatorial frameworks can illuminate complex geometric phenomena. She has made significant contributions to understanding the topology of real hypersurfaces, bitangents to plane quartics, and the birational geometry of matroids through tropical methods. Her publication record from 2013-2023 demonstrates consistent advancement in tropical geometry applications. Recent work shows increasing focus on real algebraic geometry problems through tropical lenses, with notable contributions to enumerative geometry, matroid structures, and topological properties of algebraic varieties. Shaw frequently collaborates with leading researchers including Hannah Markwig, Arthur Renaudineau, Johannes Rau, and Annette Werner. Prior to her position at the University of Oslo, Shaw held postdoctoral appointments at the Max Planck Institute Leipzig, Technical University of Berlin, and University of Toronto, as well as time at the Fields' Institute during a semester focused on Combinatorial Algebraic Geometry.

Jaakko Timo Henrik Järviمشاهده پروفایل

استاد

• Generic Programming
• Programming Language Design
• Graphical User Interfaces (GUI)
• +۷ مورد دیگر

Jaakko Timo Henrik Järvi is a Professor in the Department of Informatics at the University of Bergen, Norway, with additional affiliations at the University of Turku, Finland. His research focuses on programming language design, generic programming, and human-computer interaction, particularly in GUI frameworks and software reuse. His research interests include generic programming, programming language design (especially the Magnolia language), high-performance computing, array programming, and GUI engineering. He emphasizes formal methods and algebraic specifications to build reusable and efficient software systems. His work bridges theoretical foundations with practical applications in software development and education. The recent publications highlight a strong trend in declarative GUI frameworks, multi-selection models, and generic programming. His work explores domain-specific languages for GUI structure manipulation, reusable selection semantics across platforms, and optimizing array computations using the Mathematics of Arrays. These efforts reflect a consistent focus on software abstraction, correctness, and reusability. Jaakko Järvi has supervised doctoral students, including Tetiana Yarygina, whose dissertation explored microservice security. While no specific grants are detailed, his work on VisAST was supported by the Research Council of Norway (Project 250683), indicating active external funding. He frequently collaborates with researchers like Magne Haveraaen, Knut Anders Stokke, and Sean Parent. He contributes to tools and frameworks such as the MultiselectJS library and the VisAST educational tool. These are outcomes of collaborative research teams focused on improving software development practices and computer science education.