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.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۳۹۱-۴۰۵ از ۴۱۴ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Afaq Ahmadمشاهده پروفایل
پژوهشگر ارشد
Afaq Ahmad is a Postdoctoral Fellow at Oslo Metropolitan University , affiliated with the Faculty of Technology, Art and Design and the Department of Built Environment. His research focuses on the intersection of civil engineering, structural analysis, and machine learning applications. Institution: Oslo Metropolitan University Department: Built Environment Afaq's work examines hybrid concrete technologies, fiber-reinforced composites, and structural health monitoring using advanced computational methods. He specializes in applying image processing and neural networks to evaluate concrete properties and defects. His recent publications highlight innovations in engineered cementitious composites for masonry, symbolic matrix structural analysis for beams, and machine learning-driven assessments of concrete columns and wooden structures. Key trends include non-destructive testing and predictive modeling for infrastructure resilience. Research activities span from 2022-2025 , with 15 representative articles demonstrating expertise in: Concrete material science and failure analysis Machine learning integration in structural engineering Seismic performance evaluation Hybrid composite reinforcement systems His work frequently combines computer vision techniques with civil engineering challenges, particularly in defect detection and load capacity prediction.
Dr. Inge Jonassen is a Professor and currently serves as the Head of Department at the Department of Informatics, University of Bergen. She has overall responsibility for the department's activities including research and education. Her leadership role indicates her significant standing within the academic community and her department. Dr. Jonassen's primary research focus is in bioinformatics , specifically developing and applying informatics methods for the analysis of molecular biology data. Her work spans several key areas: Development of methods for automatic discovery of patterns in biological data Application of data analysis techniques and algorithms to molecular biology problems Machine learning approaches for biological data interpretation Analysis of diverse biological data types including DNA sequences, protein structures, and gene expression data Integration of data and tools within bioinformatics Her research demonstrates a strong interdisciplinary approach, with tight collaboration between her computational group and experimental groups across various biological and medical research fields. This collaborative model enables translation of computational methods into practical biological insights. Dr. Jonassen's publication record shows consistent output in high-impact journals, with research spanning marine biology, cancer research, toxicology, and genomics. Her work frequently involves analysis of next-generation sequencing data and development of computational tools that address specific biological challenges. A notable trend in her recent work is the increasing focus on single-cell analysis techniques and spatial biology, reflecting broader trends in the field. She teaches primarily bioinformatics courses, though she has also taught user courses in informatics and operating systems, demonstrating her versatility as an educator. Her group page indicates active research with numerous current projects.
Marco Molinariمشاهده پروفایل
پژوهشگر ارشد
Marco Molinari is a Postdoctoral Fellow in High-dimensional Statistics at the Department of Biostatistics , University of Oslo's Faculty of Medicine. His research focuses on advanced statistical modeling techniques with applications in biomedical data analysis. PhD in Statistics from University College London (2020) Specialized in Bayesian graphical models and metabolomics analysis Former Machine Learning Engineer in London (2020-2023) Research interests: Baysian Nonparametric Processes Dynamic Network Modeling Ethnic Metabolic Differences False Discovery Rate Control Publications highlight his contributions to: Statistical Methods in Medical Research Bayesian Dynamic Networks Ethnic Variation Analysis Insulin Resistance Modeling His methodological work spans computational efficiency, cross-population comparisons, and multi-omics data integration, primarily applied to cardiovascular and metabolic diseases.
- Machine Learning
- Neuroscience
- Statistics
- +۲ مورد دیگر
Aree Widya Witoelar is a Researcher at the University of Oslo , affiliated with the Neural Systems department. Previously held positions include Researcher at Oslo University Hospital (2017-2018) and Postdoctoral Researcher roles at the University of Oslo and Kavli Institute for Systems Neuroscience, NTNU. Ph.D. in Machine Learning (2005-2010), M.Sc. in Physics (2002-2005) - both from University of Groningen Key research areas: Neural process modeling, statistical machine learning, genetic mechanisms in neurodegenerative diseases Her work applies machine learning to analyze neural data and genetic factors across conditions like Alzheimer's, Parkinson's, and schizophrenia. Articles span computational neuroscience, genetic epidemiology, and molecular psychiatry, with a focus on cross-disorder genetic analyses and cortical function modeling. Scientific Awards: Marie Curie Career Development of Researchers (2016) Ubbo Emmius doctoral scholarship (2005)
Anders Hjortمشاهده پروفایل
پژوهشگر ارشد
Anders Hjort is a Doctoral Research Fellow (now PhD graduate) at the University of Oslo's Department of Mathematics within the Statistics and Data Science group. He successfully defended his PhD dissertation in February 2025, focusing on uncertainty quantification in house price prediction using machine learning techniques. His work bridges academic research and industry applications through collaboration with Eiendomsverdi, a Norwegian property technology company. His educational background includes a Master of Science in Industrial Mathematics from the Norwegian University of Science and Technology (NTNU) in Trondheim (2019), complemented by one academic year at Technische Universität Berlin. Prior to his PhD, he worked as a consultant for Deloitte. Hjort's research centers on tree-based machine learning methods, conformal prediction, and computational statistics for real estate valuation. He specializes in developing interpretable models that quantify prediction uncertainty, particularly for automated valuation systems in housing markets. His approach combines statistical rigor with practical machine learning applications to address challenges in property technology. His publication record reveals a consistent trajectory in applying conformal prediction and interpretable AI to housing market analysis. The research spans theoretical uncertainty quantification frameworks to empirical studies of Norwegian and international markets, often utilizing locally weighted methods and ensemble tree models. This work demonstrates increasing sophistication in handling spatial heterogeneity and model confidence in real estate prediction. Hjort has actively engaged with the academic community through conference presentations at COPA and WEAI events, participation in the Machine Learning Summer School near Cape Town, and a visiting researcher position at North Carolina State University's Statistics Department in fall 2023. His PhD project (2021-2024) was supervised by Associate Professors Johan Pensar and Ida Scheel, and Professor Dag Einar Sommervoll from NMBU.
Leiv Rønnebergمشاهده پروفایل
پژوهشگر ارشد
- Statistics
- Data Science
- Biostatistics
- +۲ مورد دیگر
Leiv Rønneberg is a Postdoctoral Fellow in Statistics and Data Science at the University of Oslo (UiO), with a primary affiliation at the Department of Mathematics. His research focuses on Bayesian statistical methods, machine learning, and data science, particularly applied to biomedical and astrophysical domains. Key research trends in his publications include: Development of flexible Bayesian models (FlexKnot, bayesynergy) for complex datasets Applications in 21 cm cosmological signal processing and drug combination analysis Creation of bioinformatics tools (screenwerk) for experimental design Interdisciplinary work spanning astrophysics, pharmacology, and sports science Scientific Awards: No awards explicitly mentioned in the provided text. Students: No advisees or students listed in the current description.
Michele Guerraمشاهده پروفایل
پژوهشگر ارشد
Michele Guerra is a Doctoral Research Fellow at the Department of Mathematics and Statistics , UiT The Arctic University of Norway. His research focuses on graph neural networks (GNNs) , with emphasis on explainability , temporal modeling , and subgraph-based architectures . Recent work includes applications of Koopman theory for GNN interpretation and probabilistic load forecasting using reservoir computing techniques. Research Trends: His publications highlight innovative approaches to improving the expressivity and transparency of graph-based machine learning models, particularly through stochastic methods and subgraph decomposition. Key application domains include energy systems forecasting and dynamic graph analysis . Key Collaborations: Active collaborations with researchers such as Indro Spinelli , Simone Scardapane , and Filippo Maria Bianchi are evident in recent co-authored works presented at venues like the Northern Lights Deep Learning Workshop.
Jan Zavadilمشاهده پروفایل
پژوهشگر ارشد
- Machine Learning
- Deep Learning
- Statistical Methods
- +۳ مورد دیگر
Jan Zavadil is a Research Fellow at UiT The Arctic University of Norway, affiliated with the Department of Mathematics and Statistics. He contributes to the Machine Learning Group and the project 'Transforming ocean surveying by the power of DL and statistical methods.' Research Interests Machine Learning Deep Learning Statistical Methods Ocean Surveying Computational Statistics Applied Mathematics Contact: jan.zavadil@uit.no
Per Anton Olsen is a University Lecturer at the Department of Automation and Process Technology, UiT The Arctic University of Norway. His work focuses on signal processing applications in Arctic engineering and safety systems. Institution: UiT The Arctic University of Norway Department: Automation and Process Technology Research interests include signal processing for environmental monitoring in extreme climates and antenna technology optimization. His 2022 thesis on snow detection for antenna radomes highlights his focus on Arctic-specific engineering challenges. Recent publications demonstrate expertise in data analysis and machine learning applied to infrastructure safety. While no scientific awards are publicly listed, his research contributes to critical engineering solutions in the High North.
- Machine Learning
- Deep Learning
- Applied Mathematics
- +۶ مورد دیگر
Gitta Astrid Hildegard Kutyniok is a Professor in the Department of Physics and Technology at UiT The Arctic University of Norway. Her research spans machine learning, applied mathematics, and signal processing, with a focus on theoretical foundations and practical applications.
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
Karl Øyvind Mikalsen is an Associate Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway. He is also a division leader at the Center for Patient-Near Artificial Intelligence (SPKI) at University Hospital of North Norway (UNN). His research focuses on applying artificial intelligence (AI) to healthcare, particularly in analyzing medical images, speech, and text using language models and machine learning, with applications such as anonymizing patient records and evaluating AI tools for clinical use. Research Interests: Mikalsen’s work bridges medical informatics and computer science, emphasizing explainable AI, clinical time series analysis, and medical image processing. He collaborates with UiT and UNN to integrate AI into healthcare systems. Recent Publications: His studies span AI-driven clinical coding, breast cancer detection via mammography, surgical infection prediction, and self-supervised representation learning for medical data. Key methodologies include transformer models, kernel methods for time series, and uncertainty-aware ensembles. Collaborations: Mikalsen works with researchers such as Robert Jenssen, Michael Kampffmeyer, and Arthur Revhaug, focusing on clinically relevant AI applications in Scandinavia.
Samuel Kuttnerمشاهده پروفایل
دانشیار
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.
Akriti Sharmaمشاهده پروفایل
پژوهشگر ارشد
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.