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• Space Physics
• Atmospheric Physics
• Cosmic Dust
• +7 more

Ingrid Brigitte Mann is a Professor at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research spans space physics, atmospheric physics, and dusty plasma phenomena, with a focus on cosmic dust, meteoroids, and polar atmospheric interactions. Current Projects: Cosmic dust injection into the upper Earth atmosphere MXD2 rocket project to study the mesosphere EISCAT Research infrastructure project Completed Projects: Mesospheric Dust Project Dust in the Heliosphere Project EISCAT_3D (Research Infrastructure) Her recent publications highlight cosmic dust dynamics, PMSE analysis, and Solar Orbiter/Parker Solar Probe missions. Techniques include deep learning, radar measurements, and computational modeling. She teaches courses such as FYS-2000 Quantum Mechanics and FYS-2019 Introduction to Space Physics, and leads student rocket projects like G-Chaser.

• Machine Learning
• Artificial Intelligence in Healthcare
• Digital Health & mHealth
• +7 more

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

• Medical Artificial Intelligence
• Machine Learning
• Clinical Data Analysis
• +2 more

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.

• Signal Processing
• Machine Learning
• Statistical Signal Processing
• +3 more

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.

Morten Hjorth-JensenView profile

Professor

• Computational Physics
• Many-Body Theory
• Nuclear Physics
• +3 more

Professor Morten Hjorth-Jensen is a theoretical physicist affiliated with the Department of Physics at the University of Oslo and the Department of Physics and Astronomy at Michigan State University. He has held a shared professorship between these institutions since 2012, with prior roles as an associate professor (1999) and full professor (2001) at the University of Oslo. Education: PhD in Physics, University of Oslo (1993) His research spans computational physics, nuclear many-body theory, quantum computing, and machine learning, focusing on solving Schrödinger's and Dirac's equations for complex systems. He explores algorithmic methods, quantum mechanical properties, and interdisciplinary applications of machine learning in nuclear and particle physics. Recent publications highlight his expertise in quantum computing algorithms for many-body systems, machine learning in nuclear physics, and computational modeling of neutron stars. Themes include neural networks, Bayesian methods, and quantum simulations. Supervision: Geoscience: Optimal Climate Physics: Frictional properties of surface structures generated by machine learning Machine-learning-based molecular modeling of nanoscale geological processes Quantum computing algorithms for quantum mechanical many-body systems

• High-Energy Nuclear Physics
• Particle Physics
• Quantum Chromodynamics
• +5 more

Konrad Tywoniuk is Professor and Group Leader of the Theoretical Physics Group at the University of Bergen's Department of Physics and Technology. His research bridges high-energy nuclear physics and particle physics, focusing on quantum chromodynamics (QCD) jet interactions in quark-gluon plasma, non-equilibrium dynamics of strongly coupled systems, and machine learning applications in physics. He leads the 'Thermalizing jets' project funded by the Trond Mohn Foundation and contributes to the ALICE collaboration at CERN. His research explores: Jet quenching mechanisms in heavy-ion collisions Medium-induced parton energy loss QCD matter under extreme conditions Deep learning approaches for jet tomography Non-equilibrium properties of quantum systems Recent publications demonstrate a strong focus on jet-medium interactions, with 2023-2025 work advancing theoretical frameworks for medium-induced radiation and applying machine learning to collision data. His 15 most recent articles show consistent investigation into jet suppression, azimuthal anisotropy, and precision QCD calculations. Honors include the 2018 TMS Starting Grant that established his research group. His lab leads theoretical investigations into QCD dynamics while collaborating with experimental groups at RHIC and LHC facilities. Teaching responsibilities encompass statistical physics, general relativity, and relativistic heavy-ion physics courses. He maintains active international collaborations through the ALICE experiment and joint theoretical initiatives.