جست‌وجوی استاد و پژوهشگر

Roland Olssonمشاهده پروفایل

دانشیار

• Machine Learning
• Automatic Programming
• Artificial Neural Networks
• +۳ مورد دیگر

Roland Olsson is an Associate Professor at the Department of Computer Science and Communication, Oslo and Akershus University College of Applied Sciences (HIOF). He holds a doctorate from the University of Oslo (UiO) and previously served as an Associate Professor at Chalmers University of Technology in Gothenburg, Sweden. His research focuses on machine learning, automatic programming, and algorithm design through large-scale combinatorial search, particularly via his ADATE system. Olsson’s work emphasizes meta-machine learning, including the automatic design of neural network architectures. His research group explores applications in image processing, symbolic regression, and optimization. Key areas include improving algorithms for edge detection, oil well event prediction, and real-time CNC machine control through imitation learning. Publications span domains like evolutionary computation, industrial informatics, and smart grids. His work frequently appears in journals like IEEE Open Journal of the Industrial Electronics Society and conferences such as IEEE International Conference on Big Data and Smart Computing. Though no specific awards are listed, his contributions to automatic programming and machine learning algorithms are widely recognized in the academic community. Olsson’s advising and grant involvement is implied through collaborative projects with industry partners (e.g., CNC machine operators, oil well monitoring). He leads the Machine Learning group at HIOF and teaches courses in machine learning. His research extends to interdisciplinary applications, including IoT architectures for smart grids and healthcare activity recognition.

• Structural Dynamics
• Railway Engineering
• Structural Health Monitoring
• +۷ مورد دیگر

Anders Rønnquist is a Professor and Head of the Department of Structural Engineering at NTNU's Faculty of Engineering. His research focuses on structural dynamics, railway infrastructure, and timber structures, with emphasis on reliability, safety, and innovative monitoring techniques. Collaborations include KTH Stockholm, University of Porto, and industry partners like Elektromotus and Vilnius-based entities. Research Interests Dynamic behavior of railway catenary systems and bridges Structural health monitoring using advanced sensors and machine learning Timber frame dynamics and connection stiffness modeling Wind-structure interaction in long-span bridges Conceptual form-finding in architectural engineering Recent Research Trends Recent work emphasizes data-driven approaches: Kalman filters for crosswind load estimation, deep learning for structural defect detection, and surrogate models for pantograph-catenary systems. Studies also address long-term monitoring of timber buildings and fatigue reliability of railway bridges under evolving traffic conditions. Education & Teaching Coordinates the Teknostart Master's program in Civil & Environmental Engineering. Teaches advanced courses in structural dynamics, steel structures, and railway catenary systems at NTNU. Active in interdisciplinary education bridging structural engineering and architecture. Research Groups Structural Dynamics Group Conceptual Structural Design Group

• Emergency Medicine
• Pre-hospital Critical Care
• Healthcare Quality Improvement
• +۴ مورد دیگر

Andreas Jørstad Krüger is an Associate Professor at NTNU's Institute of Circulation and Medical Imaging within the Faculty of Medicine and Health Sciences. He is also a consultant anaesthetist and air ambulance physician. His roles emphasize bridging clinical practice with academic research in pre-hospital and emergency care. University: Norwegian University of Science and Technology (NTNU) Faculty: Faculty of Medicine and Health Sciences Department: Institute of Circulation and Medical Imaging Research interests focus on improving healthcare quality through data-driven approaches, optimizing pre-hospital critical care systems, and advancing resuscitation techniques. Key topics include physician-staffed emergency medical services (EMS), trauma management, and the application of technology in emergency response. His work spans quality indicator development, simulation-based studies, and clinical trials evaluating interventions like resuscitative endovascular balloon occlusion (REBOA). Recent articles highlight his contributions to optimizing ambulance response times, evaluating dispatch precision in helicopter EMS, and refining protocols for airway management and cardiac arrest interventions. Collaborations involve Nordic institutions and emphasize multicenter studies. No scientific awards are explicitly listed, but his extensive publication record reflects sustained academic engagement. His work contributes to both clinical practice improvements and foundational research in emergency medicine systems.

• New Product Development
• Engineering Design Thinking
• Human-Machine Interaction
• +۷ مورد دیگر

Martin Steinert is a Professor of Engineering Design and Innovation at the Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Engineering and the Department of Mechanical and Industrial Engineering. He leads the Design & Engineering research group and heads TrollLABS, a prototyping lab he founded in 2014. His research focuses on the fuzzy front end of product/service development, human-machine interactions in critical environments, and disruptive technology management. Education: Ph.D. in Engineering Design. Affiliations: Norwegian Academy of Technological Sciences (NTVA). Teaching: Courses include Innovation by Design Thinking, Fuzzy Front End Engineering, and Engineering Design Methodology for PhD students. Research Interests: 3D printing and additive manufacturing for medical and aerospace applications. Prototyping methodologies and open-source hardware paradigms. Human factors in remote operations and autonomous systems. Generative AI's role in design innovation and ethical challenges. Articles Trends: Recent work emphasizes blood glucose prediction algorithms, haptic surgical simulations, and material science in 3D printing. Themes include clinical relevance in AI-driven healthcare and low-cost space prototyping. Awards: Top 1% in Mechanical Engineering (Stanford, 2022). Grants: Over $15M from NSF, Swiss NSF, and Norwegian Research Council. Advising: Supervising 9 PhD students and leading international collaborations like the CERN Challenge-Based Innovation program. Labs/Teams: TrollLABS pioneers open-source prototyping tools and hosts hackathons (e.g., IDEA Challenge). Collaborations include Stanford University and CERN.

• Urban Sustainability
• Building Energy Modeling
• Resource Efficiency
• +۵ مورد دیگر

Şahin Akın is a Research Fellow at the Faculty of Architecture and Design of NTNU, Norway, and affiliated with MIT's Building Technology Program. His research focuses on energy-positive building transformations, urban sustainability, and resource efficiency, with contributions to global initiatives like the United Nations Environment Programme (UNEP) and the International Resource Panel (IRP). Education: Ph.D. in Industrial Ecology, NTNU (Norway) M.Arch., Middle East Technical University (Turkey) B.Arch., Izmir University of Economics (Turkey) Research Interests: His work bridges architecture, engineering, and industrial ecology, emphasizing building energy modeling, positive energy districts (PEDs), and archetype-based resource use analysis. Key areas include climate-positive design strategies, Global South urban energy systems, and circular economy approaches in construction. Projects: He contributes to projects such as DUT-Making PEDs, CIRCEULAR, and CO2NSTRUCT, addressing building energy efficiency, data integration, and European Commission policy requirements. His work on the Middle Eastern residential stock integrates GIS-based modeling and dynamic stock analysis. Labs/Teams: Engaged with NTNU’s Industrial Ecology Programme and MIT Building Technology Program, collaborating on global resource efficiency frameworks and urban energy system innovations.

Tobias Johnمشاهده پروفایل

پژوهشگر

• Knowledge Representation
• Automated Planning
• Formal Methods
• +۷ مورد دیگر

Tobias John is a Doctoral Research Fellow in the Department of Informatics at the University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. He is a member of the Reliable Systems and Data and Knowledge Management (DKM) research groups, and his work is supported by the EU REMARO project. His research focuses on enhancing the robustness of controllers that interact with evolving knowledge bases, particularly OWL ontologies, using formal logic and automated planning techniques. University: University of Oslo School: Faculty of Mathematics and Natural Sciences Department: Department of Informatics Research Groups: Reliable Systems, Data and Knowledge Management (DKM) Project: REMARO (EU-funded) His research interests center on knowledge representation, automated planning, formal methods, and logic in computer science. He investigates how ontologies can be integrated into planning systems to improve reasoning under uncertainty and ensure reliable behavior in dynamic environments. His work bridges theoretical computer science with practical applications in robotics and software reliability. The recent publications of Tobias John reflect a strong trend in combining ontology-based reasoning with robust planning and testing methodologies. His work spans from theoretical automata transformations to applied risk-aware planning for marine robots and mutation-based testing of knowledge graph applications. The dominant themes include ontology-mediated planning, logic-based validation, and reliability engineering in AI systems. He has not received any explicitly mentioned scientific awards. Tobias John is actively engaged in research but there is no indication of advising students or managing grants independently. His contributions are primarily through collaborative research within the REMARO project and his publications with senior researchers such as Einar Broch Johnsen, Eduard Kamburjan, and Patrick Koopmann. He is part of the Reliable Systems and Data and Knowledge Management (DKM) research groups at the University of Oslo, which focus on dependable computing, knowledge engineering, and formal verification of complex systems.

• Breast Cancer
• Molecular Biology
• Metastasis
• +۱۵ مورد دیگر

Erik Knutsen is an Associate Professor at the Institute of Medical Biology, Faculty of Health Sciences, UiT The Arctic University of Norway. He holds a PhD in Medical Biology and a Master’s in Molecular Biotechnology. He currently serves as the Study Program Leader for the Bachelor program in Biomedicine and teaches cellular biology at undergraduate and graduate levels. His research is centered on breast cancer metastasis, focusing on molecular mechanisms such as epithelial-mesenchymal transition (EMT), tumor microenvironment, non-coding RNAs, and single-cell sequencing. He leads a major project using single-cell RNA-seq to subclassify breast cancer cells in lymph nodes for improved diagnosis and personalized treatment. His goal is to prevent overtreatment and understand tumor dormancy and recurrence. Erik’s recent publications span high-impact journals like Nature , PNAS , and Journal of Clinical Investigation , with research themes in bioinformatics, CRISPR/Cas, serglycin, miRNA regulation, and liquid biopsies. His work often involves large-scale computational analysis and collaboration with clinicians, bioinformaticians, and molecular biologists. Scientific Awards: No specific awards listed in the provided text. Erik is currently supervising two PhD students and one master student as main supervisor, and one PhD student as co-supervisor. He has previously supervised multiple master’s, bachelor’s, and Erasmus+ trainee students. His research is supported by strong national and international collaborations, including work with George A. Calin at MD Anderson. He has led projects on functional RNA profiling, miRNA variations, and transcriptome analysis in cancer. He is based in Tromsø and actively contributes to both research and education in molecular oncology.

Baltasar Enrique Beferull Lozanoمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.

• Applied Mathematics
• Mathematical Modeling
• Multiphase Flow
• +۸ مورد دیگر

Steinar Evje is a Professor of Applied Mathematics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Energy and Petroleum Technology. His research bridges mathematical modeling with applications in petroleum engineering and cancer biology. His research interests include: Mathematical modeling of multiphase flow in porous media Conservation laws and PDEs Tumor cell migration and biomechanics Fluid-structure and cell-fluid interactions Data-driven discovery of PDEs Numerical analysis and scientific computing His recent publications (2023–2024) show a strong trend in combining machine learning with physical modeling, particularly in learning flux and diffusion functions from data, solving conservation laws using graph neural networks, and modeling tumor progression under fluid pressure. These works span disciplines from petroleum engineering to computational oncology, reflecting interdisciplinary impact. Scientific awards are not mentioned in the provided text. He actively collaborates with researchers in both engineering and biomedical fields. While specific students are not listed, his extensive publication record suggests mentorship of graduate students and postdocs. He has contributed to modeling in oil recovery, well control, and tumor microenvironment dynamics, with no mention of external grants but evident sustained research output. He is involved in modeling efforts related to: Two-phase and three-phase flow in porous media Cell migration under interstitial fluid flow Spontaneous imbibition and wettability alteration Gas migration in leaking wells Low salinity flooding in oil recovery

• Pharmacology
• Neuroscience
• Neuropharmacology
• +۱۲ مورد دیگر

Parisa Gazerani is a Professor at the Faculty of Health Sciences, Oslo Metropolitan University , specializing in Pharmacology, Neuroscience, and Digital Health Technology . Her research bridges neuropharmacological mechanisms with innovative digital solutions for pain management, mental health, and food safety. Her work includes: Co-occurrent pain and psychological distress across lifespan Intelligent gamified solutions for pediatric scoliosis treatment Wearable sensors for personalized scoliosis braces Digital health implementation in emergency care Microplastic uptake in edible plants and its health implications AI-driven neuroadaptive therapies for migraine-depression comorbidities She collaborates with international institutions including the European Academy of Neurology and International Center for Neuroscience Research , focusing on translational approaches from lab to clinical practice. Her publications emphasize: Neurochemical pathways in chronic pain Drug safety biomarkers via yawning responses Brain health optimization through exposome analysis 3D bioprinting applications for glioblastoma drug screening

• Health Technology
• Patient-Reported Outcome Measures
• Clinical Interactions
• +۲ مورد دیگر

Astrid Torbjørnsen is an Associate Professor at Oslo Metropolitan University's Faculty of Health Sciences, Department of Nursing and Health Promotion. With a PhD in Health Sciences from the University of Oslo and extensive clinical experience as a registered nurse, she combines teaching and research through technology-centered approaches. She administers courses in research methods, evidence-based practice, and bachelor's thesis work, while leading the User-centred outpatient clinic project in collaboration with Akershus University Hospital. She also serves as deputy manager for the Quality of Life research group and actively engages in international education initiatives like COIL (Collaborative Online International Learning). Research Interests : Astrid specializes in health technology and patient-reported outcome measures (PROMs), particularly their impact on clinical interactions and chronic disease management. Her work spans diabetes care, digital follow-up systems, and socioeconomic health interventions. Publication Trends : Recent articles focus on digital health interventions for diabetes management, health literacy in pediatric obesity, and person-centered care technologies. These publications emphasize cross-disciplinary collaboration, evidence synthesis, and real-world implementation challenges. Labs & Collaborations : She works closely with Akershus University Hospital and OsloMet's Quality of Life Research group, integrating clinical practice with academic research in technology-driven healthcare solutions.

Anita Torabiمشاهده پروفایل

استاد

• Structural Geology
• Fault Mechanics
• Fluid Flow in Deformed Reservoirs
• +۶ مورد دیگر

Anita Torabi is a Professor in Structural Geology at the University of Oslo , affiliated with the Department of Geosciences. Her research focuses on structural geology, fault mechanics, fluid flow in deformed reservoirs, CO2 storage, geothermal reservoirs, and seismicity. She employs advanced methods such as 3D seismic analysis, fault asperity studies, and machine learning for geological applications. Key affiliations: University of Oslo, NORCE (2015-2018), Uni Research CIPR (2008-2018) Education: PhD in Geosciences (2008) - University of Bergen Research Interests include: Fault mechanics and deformation processes Fluid flow in faulted reservoirs CO2 storage and green energy solutions Geomechanics and seismic risk assessment Machine learning in structural geology Basement rock fracture analysis Recent Publications highlight trends in: 3D fault characterization using deep learning Fluid dynamics in siliciclastic-carbonate systems Seismic attribute analysis for fault geometry Compaction bands in Tertiary-Quaternary sequences Ontology-driven geological modeling Machine learning applications for CO2 storage Research Leadership includes projects such as: CO2 Seal and Bypass – COPASS CO2SafeQuest (fault behavior for CO2 storage) gigaCCS@UiO (Center of Excellence in Carbon Capture) VICCO (volcanic-sedimentary CO2 storage) Pool Studies CO2 Storage research group

• Cybersecurity
• Privacy and Data Protection
• GDPR Compliance
• +۷ مورد دیگر

Mohamed Abomhara is a Senior Researcher and Head of Department at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU) , Faculty of Information Technology and Electrical Engineering. He serves as Discipline Leader for the MRI PET (Multidisciplinary Research group on Privacy and data protEcTion) research group. Research Interests His expertise includes: GDPR compliance and privacy-by-design Risk assessment and secure system design AI ethics and social-cyber risk mitigation Healthcare digital transformation security Border control technology ethics Recent Publications His 2024-2025 research focuses on multilingual hate speech detection , border control technology acceptance , and privacy protections in national identification systems , bridging AI ethics, cybersecurity, and regulatory compliance. Earlier work (2016-2022) addresses: Cybersecurity in digital substation infrastructure Secure collaborative healthcare information sharing Blockchain-based GDPR compliance AI-driven social media analysis

• Autonomous Systems
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Muhammad Adnan serves as a Research Fellow at the Department of Technology and Safety, UiT The Arctic University of Norway, with contact details including email muhammad.adnan@uit.no and phone +47 77 66 02 47. His research spans critical domains in modern engineering and computer science, with primary focus areas: Autonomous Maritime Systems : Developing operational frameworks for remotely controlled vessels and onshore operation centers Machine Learning Applications : Advancing ensemble methods for classification, spam detection, and remote sensing Computer Vision Integration : Leveraging electro-optical/NIR sensors for maritime object detection Cybersecurity Solutions : Enhancing email security through stacking ensemble techniques Recent publications (2023-2024) reveal a concentrated research trajectory merging maritime autonomy with AI-driven solutions, demonstrating significant interdisciplinary work across navigation support systems, sensor fusion, and transfer learning applications. His output shows consistent publication in high-impact journals with practical engineering implementations. No scientific awards were documented in the provided materials. Information regarding student supervision, grant funding, or laboratory affiliations was not available in the source text.

• Control Engineering
• Automation
• Marine Icing
• +۵ مورد دیگر

Sushmit Dhar serves as an Associate Professor in the Department of Automation and Process Technology at UiT The Arctic University of Norway, focusing on control engineering and automation solutions for Arctic maritime challenges. His work bridges theoretical control methods with practical applications in extreme environments. His research spans: Classical and modern control methods (PID, LQR, MPC) State estimation and Kalman filtering Physics-Informed Neural Networks and Reinforcement Learning for decision support IoT-based automation for maritime operations Marine icing phenomena and sea spray measurement Recent publications demonstrate cutting-edge work in marine icing prediction using LiDAR and capacitive sensors, Arctic road safety systems, and optimization of maritime operations. His research integrates advanced sensing technologies with data-driven modeling to address safety challenges in cold climates, particularly through sea spray measurement innovations and route planning algorithms for fishing vessels and search-and-rescue operations. Dr. Dhar actively contributes to collaborative initiatives: Member of the Sustainable Technology and Safety (STS) research group Member of the IR, Spectroscopy, and Numerical Modelling Research Group Key participant in the SPRICE project (Multidisciplinary approach for spray icing modelling and decision support) His teaching portfolio includes foundational courses in Linear Systems (AUT-2604) and Control Engineering (AUT-2801) for Automation Engineering undergraduates, preparing students for technical challenges in Arctic engineering contexts.