جست‌وجوی استاد و پژوهشگر

• Quantum Computing
• Cybersecurity
• Artificial Intelligence
• +۵ مورد دیگر

Athanasios Vasilakos is a Professor at the University of Agder's Department of Information and Communication Technology. His research focuses on cutting-edge ICT domains including quantum computing, cybersecurity, and distributed systems. He maintains an extensive publication record with recent works concentrated in quantum machine learning, blockchain applications, and IoT security. Research interests span: Quantum computing architectures and cryptographic applications Secure federated learning frameworks for distributed networks AI-driven solutions for IoT and telecommunications systems Advanced cybersecurity techniques including steganalysis and homomorphic encryption Cross-domain applications in bioinformatics, smart grids, and autonomous vehicles Publication analysis reveals strong thematic focus on: security enhancements for emerging technologies (blockchain, quantum systems), privacy-preserving machine learning, and IoT optimization. Recent works demonstrate increasing attention to quantum-classical hybrid systems and their real-world implementations. Collaboration networks include extensive international partnerships across Europe and Asia, particularly in quantum computation, cybersecurity, and intelligent systems research.

• Host-pathogen interaction
• Molecular Immunology
• T-cell immunity
• +۳ مورد دیگر

Jaya Kumari Swain serves as an Associate Professor at the Norwegian Fisheries College, UiT The Arctic University of Norway, specializing in fish immunology and vaccinology. She leads research within the Fish Immunology and Vaccinology group, with primary focus on Atlantic salmon health. Her research interests span Host-pathogen interactions , Molecular Immunology , T-cell immunity , Vaccinology , and Novel delivery methods . Key areas include salmon lice control mechanisms, viral pathogenesis in cardiac tissues, and AI-driven diagnostic approaches for aquatic diseases. Recent publications reveal a strong trend toward translational vaccinology (particularly for salmon lice and viral pathogens) and advanced imaging techniques (ultrasound/photoacoustic microscopy). Her work increasingly integrates artificial intelligence with traditional immunology, focusing on functional changes in embryonic hearts post-infection and 3D tissue visualization. No scientific awards are listed in available records. Dr. Swain coordinates multiple research projects including VirtualStain (AI solutions for label-free tissue imaging), DNA Vaccine Development for Salmon Viruses , and Sterilization of Atlantic salmon with Deadend nanomaterials . Her teaching portfolio includes Fish Diseases (BIO-2604), Pathology (BIO-2605), and specialized modules in aquaculture health courses. She actively collaborates within the Fish Immunology and Vaccinology research group, contributing to both fundamental immunology studies and applied aquaculture solutions through projects like Antisense delivery for large-scale sterilization.

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

Krister Westlye Fjermestadمشاهده پروفایل

استاد

• Neurodevelopmental disorders
• Rare genetic disorders
• Child and adolescent anxiety treatment
• +۵ مورد دیگر

Dr. Krister Westlye Fjermestadad is a Professor in the Department of Clinical Psychology at the University of Oslo (UiO) , and holds a 20% research, teaching, and consultation position at the Frambu Center for Rare Diagnoses . With a Dr. Psychol degree from the University of Bergen (2012) , his academic career spans clinical child psychology, neurodevelopmental disorders, and therapeutic alliance research. Research Interests focus on: Psychosocial impacts of neurodevelopmental disorders on siblings and parents Rare genetic diagnoses and family mental health Anxiety treatment processes in children Therapeutic alliance dynamics Intervention efficacy for chronic illness families His article portfolio (2018-2025) reveals consistent work on sibling mental health in chronic illness contexts, CBT outcomes for youth anxiety, neurogenetic disorder comorbidities, and family communication dynamics. Key methodological approaches include randomized controlled trials , longitudinal studies , and cross-cultural collaborations . Additional roles include: Head of BFT clinic at PSI International collaborations with Yale, UC Davis, Utrecht University Cambodia mental health program development Klinefelter syndrome neuropsychological research

Stine Marie Ulvenمشاهده پروفایل

استاد

• Nutrition
• Personalized Nutrition
• Nutrigenomics
• +۵ مورد دیگر

Stine Marie Ulven is a Professor and Head of the Department of Nutrition at the University of Oslo's Faculty of Medicine. Her research focuses on nutritional interventions for cardiovascular disease prevention, nutrigenomics, and lipid metabolism. She leads the Personalized Nutrition and Prevention of Cardiometabolic Diseases research group and serves as principal investigator for multiple international projects including LongiTools (Horizon 2020) and SYSDIET. Her primary research interests include: Personalized nutrition approaches Nutrigenomics and gene-diet interactions Lipid metabolism and cardiovascular disease prevention Randomized controlled dietary intervention studies Metabolomics and transcriptomics in nutritional research Recent publications reveal consistent focus on dietary fat quality, inflammatory responses, and cardiovascular risk markers. Her work frequently employs whole transcriptome approaches to examine how dietary interventions modulate gene expression related to lipid metabolism and inflammation. Key trends include investigations into familial hypercholesterolemia, elderly nutrition, and developmental origins of health and disease. She has received research funding through major collaborative projects including: LongiTools (Horizon 2020, grant 874739) SYSDIET (Nordic Centre of Excellence) NutriFrail research initiative Professor Ulven serves as primary supervisor for multiple PhD candidates and collaborates extensively with researchers across Europe, particularly with institutions in Finland, Denmark, and France. Her departmental leadership includes directing educational programs including MED2200-Module 2 and specialized nutrition courses.

Carmen Simonsenمشاهده پروفایل

دانشیار

• Clinical Psychology
• Psychiatry
• Psychosis
• +۵ مورد دیگر

Carmen Simonsen is an Associate Professor in Clinical Psychology at the Department of Psychology, University of Oslo , with a concurrent 20% appointment as a Psychologist Specialist at the TIPS team, Special Psychiatric Outpatient Clinic, Oslo University Hospital . Her research focuses on reducing stigma and promoting recovery in severe mental illness through balanced knowledge about long-term outcomes and development of recovery-oriented interventions. Education: Profesjonsstudiet i psykologi (Cand Psychol), University of Oslo (1999-2003) Psychology BSc, Goldsmiths College, University of London (1996-1999) History of Art BA, University of York (1991-1994) Research Interests: 1. Long-term outcomes in psychotic and bipolar disorders through clinical/personal recovery and wellbeing studies. 2. Stigma reduction and anti-stigma interventions to improve patient hope. 3. Role of lived experience professionals in mental healthcare systems. Recent Research Trends: Her 2024-2025 publications demonstrate consistent focus on: Neurobiological and cognitive aspects of psychosis Stigma and disclosure strategies Long-term outcomes in bipolar/psychotic disorders Development of mobile health interventions Childhood trauma impact on treatment resistance Visual hallucinations and suicidality Scientific Contributions: • Led the COPE psychosis-bipolar project on pandemic impacts • Developed HÅP course for stigma management • Coordinated TIPS 20-year follow-up studies • Served on Schizophrenia International Research Society steering committee Teaching: • PSYC6320 – Internship • PSYC6350 – Clinical Specialization • PSYC3300 – Psychopathology • PSYC4315 – Assessment of Mental Disorders

Anne Cecilie Kjeldsen Larstorpمشاهده پروفایل

دانشیار

• Hypertension Research
• Cardiovascular Medicine
• Blood Pressure Monitoring
• +۵ مورد دیگر

Anne Cecilie Kjeldsen Larstorp serves as an Associate Professor in the Department of Medical Biochemistry at the University of Oslo's Faculty of Medicine. Her work is deeply embedded in cardiovascular research with a particular focus on hypertension management, blood pressure monitoring technologies, and medication adherence. She maintains an active research presence with numerous publications spanning clinical trials, biomarker discovery, and innovative diagnostic approaches in cardiovascular medicine. Dr. Larstorp's research interests center on hypertension pathophysiology and treatment, with emphasis on medication adherence assessment through therapeutic drug monitoring, novel blood pressure monitoring technologies including cuffless devices, and cardiovascular risk assessment in hypertensive patients. Her work frequently explores gender differences in hypertension outcomes, renal denervation as a treatment approach for resistant hypertension, and biomarkers for early detection of cardiovascular and renal complications. She has made significant contributions to large-scale studies including the LIFE Study and VALUE Trial, examining long-term cardiovascular outcomes in hypertensive populations. Her recent publication trends show a strong focus on practical clinical applications, with numerous studies on the OUT-ACS and OUT-POC projects investigating rapid rule-out protocols for acute myocardial infarction in primary care settings. She has also published extensively on therapeutic drug monitoring approaches to assess medication adherence in resistant hypertension, novel biomarkers for kidney damage in hypertensive patients, and the development of advanced blood pressure monitoring technologies. Her work consistently bridges laboratory research with clinical application, demonstrating translational impact across cardiovascular medicine. Dr. Larstorp collaborates extensively with the Cardiovascular and Renal Research Group and the Internal Medicine Research Group at the University of Oslo. She is actively involved in the OUT-ACS study (Faster resolution of acute myocardial infarction at the Emergency Department in Oslo), which aims to improve diagnostic pathways for acute coronary syndromes in emergency settings. Her work frequently involves multidisciplinary collaborations with clinicians, pharmacologists, and biomedical engineers to develop and validate new approaches to hypertension management and cardiovascular risk assessment.

• 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

• Computer Networking
• Quality of Experience (QoE)
• Video Streaming
• +۳ مورد دیگر

Bjørn Jonny Villa serves as Associate Professor at the Norwegian University of Science and Technology (NTNU), specializing in computer networking with emphasis on adaptive video streaming and Quality of Experience (QoE) optimization. His research addresses critical challenges in home networks, public WiFi security, and bandwidth management, as evidenced by publications spanning 2010-2014 in top venues including IEEE, Springer, and international journals. Villa earned his PhD from NTNU in 2014 with the dissertation "Enhancing Quality Aspects of Adaptive Video Streaming in Home Networks," establishing foundational work for his subsequent research. His academic journey reflects deep specialization in network performance optimization for multimedia delivery systems. Core research interests include adaptive HTTP video streaming, QoE measurement and optimization, network security vulnerabilities (particularly in public WiFi), and active probing techniques for bandwidth estimation. Villa employs experimental user studies and traffic analysis to develop practical solutions for improving streaming fairness and network resource allocation, with significant contributions to understanding how burst durations and traffic shaping impact user-perceived quality. Analysis of Villa's publication timeline reveals consistent focus on video streaming challenges: early work (2010-2011) established monitoring frameworks and home gateway optimization, mid-period research (2012-2013) advanced fairness algorithms and traffic shaping, while his 2014 output expanded into security implications of public networks. His collaborative approach is evident through recurring partnerships with Poul Einar Heegaard and Anders Instefjord across multiple publications. No scientific awards are documented in the available records. Similarly, no information regarding research grants or student supervision appears in the provided materials. Villa actively engages with the research community through conference presentations including Forskningsdagene (2013) and NIK conferences, and contributes to public discourse through media appearances in Aftenposten and Inside Telecom. His work operates within NTNU's telecommunications research ecosystem, focusing on practical implementations of network optimization techniques for real-world video delivery systems.

• 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.

Karina Standahl Olsenمشاهده پروفایل

پژوهشگر

• Nutritional Epidemiology
• Cancer Research
• Gene Expression Analysis
• +۳ مورد دیگر

Karina Standahl Olsen is a researcher affiliated with the University of Oslo's Faculty of Health Sciences, specializing in nutritional epidemiology and cancer genomics. Her work integrates systems biology approaches with large-scale population studies to investigate molecular mechanisms linking lifestyle factors, gene expression, and chronic disease risks. Her research focuses on dietary patterns, inflammatory biomarkers, and metabolomic profiling in relation to cancer development and progression. Notable projects include the NOWAC Post-genome Cohort and the Clinical and Multi-omic (CAMO) cohort, which employ transcriptomic, metabolomic, and epidemiological methods to uncover actionable insights in public health and oncology. Key publication trends reveal expertise in blood gene expression analysis, dietary risk factors for cancers (especially breast and endometrial), and metabolite-cancer associations across European populations. Her work spans multi-omics integration, precision cancer screening, and population-based biomarker discovery. Olsen's contributions appear in high-impact journals through collaborations with the European Prospective Investigation into Cancer and Nutrition (EPIC) and other major consortia. She has developed interactive tools for genomic data exploration in epidemiology.

• Reverse Logistics
• Sustainable Logistics
• Closed-Loop Supply Chain
• +۱۰ مورد دیگر

Xu Sun is a Postdoctoral Fellow at the Department of Industrial Engineering, UiT The Arctic University of Norway, Campus Narvik. His research bridges logistics, sustainability, and digital innovation within Industry 4.0/5.0 frameworks, with a focus on practical applications in Norwegian contexts including electric vehicle infrastructure and pandemic response. His research portfolio spans Reverse Logistics, Sustainable Logistics, Closed-Loop Supply Chains, Digital Twins, and Industry 5.0 integration. Key themes include applying generative AI to logistics network design, optimizing charging infrastructure for electric trucks, and developing digital twins for circular economy systems. His work emphasizes multi-objective decision-making to balance environmental, social, and economic sustainability in logistics operations. Analysis of Xu Sun's publication trends reveals a strategic shift toward human-centric digital solutions in logistics. Recent works integrate generative AI, digital twins, and simulation to address Industry 5.0 challenges, with notable emphasis on electric mobility infrastructure (2024-2025) and pandemic-related logistics (2021-2022). His research consistently employs case studies from Norway, demonstrating practical applicability while advancing theoretical models for sustainable supply chains. No scientific awards were documented in the source material. While specific student advising details are absent from the profile, Xu Sun's collaborative publication record indicates active mentorship within research teams. His projects frequently involve multi-institutional partnerships and industry engagement, though explicit grant information is not provided in the available text. Xu Sun contributes to the ArcLog research group's Intelligent Manufacturing and Logistics team and leads the 'Industry 5.0 enabled Smart Logistics' project. His work leverages tools like AnyLogic simulation and digital twin technology to develop human-centered logistics solutions, with physical operations based in Campus Narvik office D2150.

• Machine Learning
• Health Informatics
• Cancer Research
• +۵ مورد دیگر

Jan Franz Nygård is an Associate Professor at the Department of Physics and Technology , UiT The Arctic University of Norway. His work bridges Machine Learning with Health Informatics , focusing on cancer registries, AI for medical diagnostics, and data-driven healthcare solutions. UiT The Arctic University of Norway Department of Physics and Technology His research spans Cancer Research , Artificial Intelligence , and Data Science , with recent studies on synthetic data applications, federated learning, and quantum computing in healthcare. Over 15 recent publications highlight his contributions to breast cancer detection, cervical cancer risk prediction, and software testing for medical systems. Key collaborations include partnerships with the Cancer Registry of Norway , international consortia on federated networks, and interdisciplinary teams in Medical Imaging and Bioinformatics . His work addresses challenges in Secure Health Data Analysis and Population Health Management .

• Machine Learning
• Medical Imaging
• Positron Emission Tomography (PET)
• +۳ مورد دیگر

Samuel Kuttner is an Associate Professor II (part-time) in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. He specializes in applying artificial intelligence to medical imaging challenges, with core expertise in Positron Emission Tomography (PET), deep learning algorithms, and quantitative image analysis. His work bridges machine learning with clinical applications in oncology and neurology. Research Focus: Kuttner leads innovative projects at the intersection of AI and medical physics. His primary interests include: Developing non-invasive methods for arterial input function estimation in dynamic PET imaging Implementing deep learning for tumor segmentation and recurrence prediction in glioblastoma Optimizing quantitative PET/MRI through artifact correction and reconstruction algorithms Advancing physics-informed machine learning models for biomedical applications Publication Trends: His recent articles (2020-2025) demonstrate strong focus on: AI-driven solutions for PET quantification challenges Clinical translation of radiomics and deep learning in neuro-oncology Methodological innovations for preclinical and clinical imaging workflows Interdisciplinary collaborations across machine learning, physics, and clinical medicine Research Groups: He actively contributes to the Nuclear Medicine and Radiation Biology (NMRB) group and the Machine Learning Group at UiT, fostering cross-disciplinary research in medical technology innovation.