جست‌وجوی استاد و پژوهشگر

Trym Vegard Haavardsholmمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Robotics
• +۳ مورد دیگر

Trym Vegard Haavardsholm is a 20% part-time Lecturer at the University of Oslo (UiO) within the Section for Autonomous Systems and Sensor Technologies. He also serves as Principal Scientist at the Norwegian Defence Research Establishment (FFI) and is a PhD candidate at the Department of Engineering Cybernetics, NTNU. His research focuses on computer vision, machine learning, robotics, and image analysis, with a particular emphasis on multispectral imaging systems and unmanned aerial vehicles (UAVs). Haavardsholm has contributed to advancements in sensor technologies for tactical reconnaissance, autonomous navigation, and real-time data processing. His work spans applications in defense, environmental monitoring, and emergency response systems. Research Interests Haavardsholm’s research integrates interdisciplinary approaches to develop innovative sensor systems and algorithms. Key areas include compact multispectral imaging for small UAVs, in-operation camera calibration, and anomaly detection in hyperspectral data. His contributions emphasize practical applications such as urban feature classification, collaborative indoor navigation, and bioaerosol detection. His work bridges theoretical computer science with applied engineering, addressing challenges in autonomous systems and sensor fusion. Publications His publications highlight trends in multispectral sensor design, UAV imaging, and real-time georeferencing. Recent work includes compact sensor systems for tactical use and advancements in pushbroom image rectification. Earlier research explored band selection algorithms for target detection and GPU-accelerated anomaly detection. Professional Roles As a lecturer, Haavardsholm contributes to academic supervision and teaching in autonomous systems. His dual role at FFI and UiO reflects his commitment to translating academic research into practical defense and civilian applications. His PhD candidacy at NTNU underscores his ongoing academic engagement in engineering cybernetics.

Jan Butsمشاهده پروفایل

دانشیار

• Translation Theory
• Medical Humanities
• Conceptual History
• +۵ مورد دیگر

Jan Buts is an Associate Professor at the Sustainable Health Unit within the Faculty of Medicine at the University of Oslo, specializing in the intersection of translation theory, medical humanities, and corpus linguistics with a focus on sustainable health and development discourses. His academic background includes: Linguistics and Literature studies at KU Leuven (Belgium) PhD in Translation and Intercultural Studies from the University of Manchester (UK) Postdoctoral research at Trinity College Dublin (Ireland) Assistant Professor position at Boğaziçi University (Turkey) Buts' research centers on corpus-assisted discourse analysis applied to translation theory, medical humanities, and sustainable development. He actively develops the Sustainability and Health Corpus to analyze how language shapes public health narratives, gender discourses in development aid, and conceptual frameworks around sustainability. His work bridges linguistic analysis with critical perspectives on social justice and health equity. Analysis of his recent publications reveals a strong trajectory toward integrating corpus linguistics with medical humanities and sustainable development goals. Key trends include examining gendered vulnerabilities in policy discourse, translation's role in knowledge dissemination for sustainability, and digital media's impact on health communication – all characterized by methodological innovation in corpus construction and conceptual analysis. His scientific recognition includes: Runner-up for the Martha Cheung Award (2023) Buts serves on the executive council of IATIS (International Association for Translation and Intercultural Studies) and leads multiple research initiatives including the SHE Corpus project. His grant activities focus on interdisciplinary collaborations between translation studies and medical humanities, particularly through projects like KNOWIT (Knowledge in Translation) and MEDRA (Medicalisation of Democratic Rights in Abortion debates). He directs the Sustainable Health Unit, fostering cross-disciplinary teams that combine linguists, medical researchers, and public health experts to advance data-driven critical analysis of health and sustainability discourses through innovative corpus methodologies.

• 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.

Mohan Lal Kolheمشاهده پروفایل

استاد

• Smart Grid
• Renewable Energy Systems
• Electric Vehicles
• +۵ مورد دیگر

Professor Mohan Lal Kolhe is a distinguished academic at the University of Agder , serving as a Full Professor in Smart Grid and Renewable Energy within the Faculty of Engineering and Science and the Department of Engineering Sciences . With over three decades of international academic experience, he has held positions at prestigious institutions including University College London, University of Dundee, and Hydrogen Research Institute in Canada. His career spans technical innovation, policy development (e.g., as a member of South Australia’s Renewable Energy Board), and extensive research leadership in sustainable energy systems. Research Leadership : Focus on Smart Grid integration, Electric Vehicles, Hydrogen Energy, Solar/Wind Systems, and Techno-Economic Energy Analysis. Global Recognition : Listed in the top 2% of scientists worldwide (2020-2023) by Stanford University, with 10 publications averaging 200+ citations. Recent publications emphasize advanced optimization techniques for renewable integration, EV charging infrastructure, hydrogen production, and power system stability. His work has secured competitive funding from entities like the Norwegian Research Council and EU programs. Awards and Expert Roles : Top 2% Global Scientist (Stanford, 2020-2023) Highly Cited Researcher (Top 10 publications, 200+ avg. citations) Expert evaluator for European Commission, Royal Society London, EPSRC, and Cyprus Research Foundation He actively contributes to international conferences as keynote speaker and editorial board member, with leadership roles in research groups like Autonomous and Cyber-Physical Systems and Energy Systems .

Benjamin Ricaudمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +۳ مورد دیگر

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

• Wireless Communications
• Machine Learning
• Digital Twins
• +۵ مورد دیگر

Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).

Andrea Arcuriمشاهده پروفایل

استاد

• Software Testing
• Software Engineering
• Cloud Computing
• +۶ مورد دیگر

Andrea Arcuri is a Professor at the School of Economics, Innovation and Technology within Kristiania University of Applied Sciences . His research focuses on Software Testing , Software Engineering , and Cloud Computing , with a particular emphasis on automated testing techniques for web APIs. Specialized in RESTful, GraphQL, and RPC API fuzzing Developer of the open-source EvoMaster testing tool Pioneer in integrating evolutionary algorithms and symbolic execution for test generation Active in bridging academic research with industrial software testing practices His recent publications show a strong focus on Search-Based Software Testing (SBST) , Automated Test Generation , and Industrial Adoption of Testing Technologies . He has contributed extensively to improving testability through mock generation and database handling in API testing. While no formal scientific awards are listed in the public records analyzed, his work has been consistently published in top-tier software engineering venues since 2013. The articles demonstrate increasing sophistication in fuzzing techniques, with recent extensions to handle complex dependencies like MongoDB and SQL databases.

Knut Håkon Holeمشاهده پروفایل

دانشیار

• Radiology
• Oncology
• Medical Imaging
• +۴ مورد دیگر

Knut Håkon Hole is an Associate Professor at the University of Oslo's Department of Radiology and Nuclear Medicine. His research focuses on diagnostic imaging applications in oncology, particularly in prostate and rectal cancers. He specializes in MRI, PET, and radiogenomics techniques to assess tumor biology, treatment response, and recurrence. Expertise: Prostate cancer imaging, neoadjuvant therapy response, tumor hypoxia, and imaging biomarkers Key affiliations: Oslo University Hospital (Rikshospitalet), Radium Hospital Research interests include: Developing MRI and PET protocols for cancer staging and recurrence detection Integrating imaging with genomic data (radiogenomics) Optimizing therapeutic approaches using imaging biomarkers Recent work highlights: Prostate cancer radiogenomics and hypoxia biomarkers (2024) MRI/PET comparisons for tumor localization (2021-2023) Neoadjuvant therapy response assessment in rectal and breast cancers (2020-2023) Publications span over 50 peer-reviewed articles with a focus on translational imaging research. Collaborates extensively with oncology and urology teams.

Lokukaluge Prasad Pereraمشاهده پروفایل

استاد

• Maritime Technology
• Offshore Systems
• Advanced Data Analytics
• +۶ مورد دیگر

Lokukaluge Prasad Perera is a Professor in Maritime Technology at UiT The Arctic University of Norway and a Senior Research Scientist in Smart Data at SINTEF Digital . He holds a BSc in Mechanical Engineering from Oklahoma State University (1999), MSc in Systems & Controls from the same institution (2001), and a PhD in Naval Architecture and Marine Engineering from Technical University of Lisbon (2012). His research focuses on Maritime and Offshore Systems , Advanced Data Analytics , Autonomous Navigation , Energy Efficiency , and Digital Twin Applications . He has published over 100 peer-reviewed papers and was recognized in the World's Top 2% Scientists (2021-2022) by Stanford University. Key professional experiences include roles at SINTEF Ocean (2014–2017), Center for Marine Technology and Engineering in Portugal (2008–2012), and Wärtsilä Finland (2012–2014). He has also held academic positions at Naval & Maritime Academy and Ocean University of Sri Lanka . His work addresses challenges in emission reduction , renewable energy integration , and safety-critical systems for maritime operations. Current projects emphasize trustworthiness of autonomous ships and data-driven decision frameworks for energy efficiency.

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.

• Maritime safety management and leadership
• Maritime accident and casualty analysis
• Maritime Autonomous Surface Ships (MASS)
• +۳ مورد دیگر

Tae Eun Kim is an Associate Professor in Maritime Safety Management at UiT The Arctic University of Norway, working within the Department of Technology and Security. Her research, teaching, and industrial collaboration focus on maritime safety and human factors, with particular expertise in maritime safety management, accident analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. Dr. Kim's research spans four interconnected domains: maritime safety management and leadership, maritime accident and casualty analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. She has developed assessment instruments like the Safety Leadership Self-Efficacy Scale (SLSES) and conducted STAMP-based causal analyses of maritime accidents. Her work on MASS addresses safety challenges in mixed navigational environments and examines leadership competencies for autonomous shipping operations. Her human factors research explores how technological advancements impact navigators' performance, crew dynamics, and safety outcomes, including gender parity issues in the maritime industry. Dr. Kim's publication record reveals a strong focus on the intersection of maritime safety, technology, and human performance. Her recent work increasingly addresses autonomous shipping technologies, with numerous publications on AI decision transparency, learning analytics in maritime simulator training, and multi-modal data analysis for nautical skill development. She has conducted systematic reviews on simulator training approaches and scenario design, contributing significantly to methodology development in maritime education and training. Her research demonstrates a clear trajectory toward integrating emerging technologies with traditional maritime safety practices as the industry transitions toward greater automation. Dr. Kim is actively involved in several significant research projects, including the i-MASTER EU Horizon Europe Research and Innovation Project, the REFRAME project, and the SPRICE project (Multidisciplinary approach for spray icing modelling). She is a member of both the Advanced Maritime Ship Operations research group and the Maritime Safety Science (MARSCI) Research Group, demonstrating her commitment to collaborative research in maritime safety science. Dr. Kim teaches several specialized courses at UiT, including SVF-3206 Safety Management and Accident Investigation, TEK-3014 Navigation Technology, MFA-2100 Maritime Digitalization, MFA-8010 Maritime HTO (Human-Technology-Organisation) and Innovation, and MFA-2018 Maritime Administration and Leadership. Her teaching portfolio reflects the interdisciplinary nature of her expertise, bridging engineering, safety science, and organizational behavior in maritime contexts.

• Electrical Engineering
• Lighting Technology
• Energy Efficiency
• +۶ مورد دیگر

Associate Professor Eilif Hugo Hansen works at the Department of Electrical Energy, Norwegian University of Science and Technology (NTNU). His primary research areas are electrical installations and lighting technology, with a focus on energy efficiency, daylight utilization, and applications in aquaculture. He has contributed to standardization committees like NEK/NK64 and IEC TC64, and co-developed the LYSSTYR lighting control software. Department: Electrical Energy School: Faculty of Information Technology, Mathematics, and Electrical Engineering University: Norwegian University of Science and Technology Education includes a Civil Engineering degree (1985) and Doctorate in Engineering (1990) from NTH (now NTNU). He is a certified electrical contractor with expertise in low-voltage systems. Research Highlights : Over 30 years of publications in lighting technology, electrical safety, and energy systems. Key themes include ground fault detection , non-radial electrical networks , and daylight integration in buildings. His work with Lysforsk center and SINTEF collaborations underscores his industry impact. Teaching involves courses like TET4165 Light and Lighting and TET4170 Electrical Installations . He has supervised over 40 master's theses, including topics on smart lighting , power distribution , and energy-saving technologies . Professional Engagement : Member of NEK/NK64 Low-Voltage Installations committee Contributor to IEC TC64 MT12 standards Former head of the Energy Transformation and Electrical Installations program (1997-2000) Co-developer of the LYSSTYR software for simulating daylight rhythms

• Control Theory
• Model Predictive Control
• Power Electronics
• +۳ مورد دیگر

Morten Hovd is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on advanced control systems, particularly in model predictive control, optimization, and power electronics. He has contributed to control design for uncertain systems, bilinear models, and modular multilevel converters. Research Interests Control Theory and Model Predictive Control (MPC) Optimization Techniques in Control Systems Power Electronics and Smart Grid Applications Stability Analysis of Hybrid and Discrete-Time Systems Teaching TTK4210 - Advanced Control of Industrial Processes TK8118 - Mini-seminar in Cybernetics

• Multi-Phase Flow
• Turbulence
• Lagrangian Dynamics
• +۳ مورد دیگر

Rohith Jayaram is a Postdoctoral Fellow in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU), specializing in multi-phase flows and turbulence dynamics. His research focuses on particle-laden flows, bubble-turbulence interactions, and computational fluid dynamics. Education: PhD in Particle suspensions from NTNU MSc in Aerospace Mechanics from ISAE-SUPAERO, France BEng in Mechanical Engineering from BMS College of Engineering, India His work investigates Lagrangian dynamics of solid particles and spheroids in evolving Taylor-Green vortex flows, with recent emphasis on bubble-turbulence interactions. He develops numerical algorithms for flow simulations and contributes to turbulence modeling in both 2D and 3D systems. Research trends include computational modeling of multi-phase flows, DNS validation of particle behavior in unsteady vortices, and extension to spheroid alignment dynamics. Publications span leading journals like Physics of Fluids and Physical Review E . Scientific Awards: Selected as Featured Article in Physics of Fluids (2020) Outreach activities include conference lectures at the European Fluid Dynamics Conference (2024) and European Turbulence Conference (2019). Contact: 238 Kjemiblokk 5 Gløshaugen, Trondheim; rohith.jayaram@ntnu.no

• Robotics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Danesh Tarapore is an Associate Professor at the University of Southampton specializing in robotics and AI. He focuses on human-robot interaction, swarm intelligence, and autonomous systems. His current research involves developing resilient robotic teams and optimizing learning algorithms for constrained environments. He supervises 6 PhD students in the iPhD MINDS and Computer Science programs. Dr. Tarapore's work bridges theoretical advancements with practical applications in autonomous navigation, multimodal dataset creation, and quality-diversity optimization. His publications span conferences like HRI and journals in robotics and AI. He collaborates with institutions like the University Hospital Southampton and the Boldrewood Innovation Campus. Research Interests: Human-robot collaboration, swarm systems, machine learning, and adaptive control Key Contributions: HRI-SENSE dataset, evolutionary subset selection algorithms, forest navigation frameworks Grants and Funding: Active projects in multi-agent systems and resilient robotics Dr. Tarapore maintains active roles in the robotics community through conference participation and interdisciplinary collaborations.