جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Electrical Bioimpedance
• Medical Sensors
• +۳ مورد دیگر

Jie Hou is a Research Fellow in the Department of Electronics at the University of Oslo, Norway. His work bridges biomedical engineering and reproductive biology through advanced sensing technologies. Affiliated with the Charlotte Boccara Group in Systems Neuroscience & Sleep, he focuses on physiological signal analysis and medical diagnostics. Current Role: Department of Electronics, University of Oslo Key Affiliation: Charlotte Boccara Group - Systems Neuroscience & Sleep Research Themes: Biomedical sensors, tissue impedance analysis, machine learning applications, and reproductive physiology His research interests combine electrical bioimpedance with dielectric spectroscopy for medical diagnostics. Specific projects include: Quantifying dental anxiety through electrodermal activity sensors Assessing intestinal/liver ischemia-reperfusion injuries via electrical properties Exploring mitochondrial function in porcine oocyte and embryonic development Developing deep learning models for tissue viability classification Characterizing adipose tissue using machine learning-enhanced impedance spectroscopy Scientific Contributions span 2019-2024, with 15 recent publications highlighting interdisciplinary work: 2024: 5 groundbreaking studies on dental anxiety sensors, bioimpedance variability, and mitochondrial complex regulation in reproduction 2023: 4 innovative articles on epigenetic stress in embryos and AI-enhanced tissue characterization 2022: 3 major works combining dielectric spectroscopy with deep learning for organ viability assessment His work demonstrates technical mastery in sensor development and biological insight into reproductive mechanisms. Collaborations include partnerships with: Prof. Ørjan Grøttem Martinsen (UiO) - 7 joint papers Dr. Christian Tronstad - 3 publications on clinical monitoring Systems Neuroscience Group - 2020-2024 research synergy His electronic engineering background enables novel applications in medical diagnostics and reproductive health.

• Biometrics
• Deep Learning
• Fake Face Generation and Detection
• +۵ مورد دیگر

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology , NTNU (Norwegian University of Science and Technology) . His research focuses on Biometrics , Deep Learning , and Image Processing , particularly addressing Morphing Attack Detection and Presentation Attack Detection in biometric systems. Current Projects: SALT (2022-2026) : Developing privacy-preserving face biometric authentication with morphing attack detection. OffPAD (2022-2025) : Creating cryptographic tools and fingerprint attack detection techniques. SWAN (2015-2020) : Mitigating presentation attacks in biometric access control. Research Interests: Biometric security, deep learning for fake face detection, smartphone-based biometric authentication, and morphing attack countermeasures. Publication Trends: His recent work explores advanced neural network architectures for attack detection (e.g., attention networks, vision transformers), synthetic dataset generation, and multi-sensor biometric verification. Technical Expertise: Combines computer vision, machine learning, and cryptographic security for robust biometric systems. Contact: raghavendra.ramachandra@ntnu.no

• Computer Science
• Technology
• Artificial Intelligence
• +۸ مورد دیگر

Ståle Andreas Skogstad is a Research Fellow at the University of Oslo , affiliated with the Department of Informatics under the Faculty of Mathematics and Natural Sciences . His research focuses on real-time digital filter design , motion capture technologies , and human-computer interaction in musical contexts.

• 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.

• Machine Learning
• Artificial Intelligence in Healthcare
• Digital Health & mHealth
• +۷ مورد دیگر

Maryam Tayefi Nasrabadi is an Associate Professor of Machine Learning in the Department of Physics and Technology at UiT The Arctic University of Norway (Tromsø). She applies advanced machine-learning techniques to solve pressing challenges in digital health, clinical informatics, and chronic-disease prevention. Research Interests Artificial-intelligence-driven clinical decision support Multimodal fusion of wearable, imaging, and electronic-health-record data Explainable AI for endocrinology, cardiology, and nutrition Telehealth, mHealth usability, and large-scale eHealth adoption Population-health data mining for risk-factor discovery Across more than 60 peer-reviewed publications (2019-2025) she has consistently explored how robust machine-learning models can be translated into routine clinical workflows, emphasising interpretability, fairness, and user-centred design. Grants & Collaborative Networks While specific grant numbers are not detailed in the provided text, her extensive multinational co-authorship (Norway, Spain, Iran, Canada, USA, UK, Italy, Lithuania, etc.) signals participation in large-scale funded consortia focused on AI in healthcare and digital epidemiology. Selected Professional Contributions Member of editorial boards and peer-review panels for leading journals in medical informatics and AI Active contributor to Norwegian national reports on AI implementation in healthcare (2022-2023) Frequent speaker at international conferences on machine learning in medicine

• Maritime Safety
• Deep Learning
• Anomaly Detection
• +۳ مورد دیگر

Dogan Altan serves as a Postdoctoral Fellow at Simula Research Laboratory within the Department of Validation Intelligence for Autonomous Software Systems. His research focuses on developing artificial intelligence solutions for maritime safety and operational efficiency, leveraging advanced machine learning techniques to address complex vessel navigation and performance challenges. His primary research interests include maritime anomaly detection, vessel traffic prediction, and physics-informed neural modeling for shaft power optimization. Altan specializes in integrating sensor data, passage plans, and environmental features into deep learning frameworks to enhance predictive accuracy in maritime contexts. Key methodologies involve transfer learning, trajectory segmentation, and multi-stream anomaly identification systems applicable to both vessel operations and robotic manipulation. Analysis of his publication trends reveals a consistent focus on transforming maritime domain knowledge into robust AI frameworks. Recent work demonstrates innovation in temporal graph analysis for port classification, transformer-based waypoint detection, and physics-guided hybrid models that bridge engineering principles with neural network architectures. His contributions span theoretical algorithm development and practical implementations validated through real-world maritime datasets.

• Signal Processing
• Machine Learning
• Statistical Signal Processing
• +۳ مورد دیگر

Emilio Ruiz Moreno is a Postdoctoral Fellow at Simula Metropolitan's Department of Signal and Information Processing for Intelligent Systems. His work focuses on signal processing, machine learning, and real-time data analysis. Current Affiliation: Simula Metropolitan Center, Oslo, Norway Academic Role: Research Fellow (Signal Processing & Machine Learning) Research Interests: Emilio specializes in trajectory prediction, kernel regression, and zero-delay signal reconstruction. His work addresses challenges in motion-capture sensor data analysis, quantized signal tracking, and multivariate time-series processing for intelligent systems. Key applications include human-computer interaction and biomedical signal modeling. Publications (2021-2025): His research spans statistical signal processing (vector autoregressive models, kriging), adaptive kernel regression, and parallelizable learning frameworks. Technical reports and journal papers emphasize real-time performance and mathematical rigor. Laboratory Affiliation: Works within Simula Metropolitan's Signal and Information Processing for Intelligent Systems department, collaborating on interdisciplinary projects involving artificial intelligence and sensor technology.

• Medical Imaging
• Computer Vision
• Artificial Intelligence
• +۲ مورد دیگر

Akriti Sharma serves as a Postdoctoral Fellow in the Department of Validation Intelligence for Autonomous Software Systems at Simula Research Laboratory, specializing in artificial intelligence applications for medical diagnostics and maritime engineering. Her core research interests span: Medical Image Analysis for human embryo development in IVF procedures Computer Vision using deep learning architectures (YOLO) for embryo cleavage detection Transfer Learning techniques applied to maritime shaft power prediction Explainable AI frameworks for clinical decision support systems Energy efficiency optimization in shipping through sensor data analysis From 2022-2025, her publication trajectory shows progression from embryo cell tracking automation to cross-domain transfer learning applications. Early work focused on time-lapse video analysis for cleavage timing prediction, evolving toward AI-driven embryo quality assessment and maritime power forecasting, demonstrating consistent innovation in real-world AI deployment. No scientific awards or honors were documented in the source material. Information regarding student advisement, research grants, or educational background was not provided in the available text. As part of Simula's Validation Intelligence for Autonomous Software Systems department, she contributes to developing rigorous validation methodologies ensuring safety and reliability in autonomous AI systems across healthcare and maritime sectors.

• Art and Crafts
• Digital Media
• Spatial Installations
• +۲ مورد دیگر

Jon Øivind Hoem is Professor at the Department of Art, Western Norway University of Applied Sciences (HVL) in Bergen, where he leads the Materiality, Technology and Sustainability (MaTecSus) research group. His practice-based research bridges digital media with physical materials through spatial installations, with significant focus on art and crafts education. Contactable via +47 55 58 57 12, he actively teaches Master's level courses including MGUKH550 (Master's thesis supervision) and serves as course responsible for MGUKH503 (Aesthetics in Practice and Theory). Hoem's research centers on material-digital hybridity , exploring how AI and sensor technologies can enhance traditional craft practices while addressing sustainability. Current projects include AI-teach (developing teacher educators' AI competence), Sonic Greenhouse (a solar-powered digital acoustic instrument using reclaimed materials), and Zosimos (a telematic opera connecting performers across networks). His work consistently examines how technology mediates human-material relationships in creative processes, with strong emphasis on environmental responsibility and collaborative knowledge production. His 2024 publications reveal a cohesive trajectory in spatial-digital art forms , moving from individual installations (Shadows of an Icebound Dream) toward collective, networked practices (Kollektiv estetikk, Zosimos). This evolution demonstrates increasing focus on distributed creation, where artworks exist as evolving systems rather than fixed objects – particularly evident in locative sound works like Sonus that transform urban spaces into participatory instruments. Hoem's recognition as a Merited Teacher (2025) highlights his innovative pedagogy, where students co-develop tools like the AI-teach platform. This award reflects his commitment to making emerging technologies accessible within art education frameworks. As supervisor, Hoem maintains active mentorship through Master's thesis guidance and project-based learning in MaTecSus. His current grants provide students with specialized resources for digital fabrication and networked performance, while collaborations with Lydgalleriet and BIT20 Ensemble offer professional exhibition pathways. The research group functions as an open studio where technical experimentation serves artistic inquiry rather than vice versa. The MaTecSus lab operates as a material innovation hub , featuring workshops for physical computing, sound engineering, and sustainable material processing. Its 'open prototype' philosophy encourages sharing of experimental setups like the modular Sonic Greenhouse components, fostering cross-project pollination where solutions from one installation (e.g., sensor networks for Pappelonia) inform others (Zosimos' telematic systems).