Find Academic Supervisors

Filippo SanfilippoView profile

Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +7 more

Filippo Sanfilippo is a Professor at the Department of Engineering Sciences, University of Agder (UiA), Grimstad, Norway. He holds a PhD in Engineering Cybernetics from NTNU, specializing in robotic control systems. His research focuses on robotics, wearables, human-robot collaboration, AI, and control theory, with emphasis on applications in healthcare, Industry 5.0, and emergency response. He leads projects like SAKO (Service Robots in Municipal Health Services) and the EMERALD initiative (3D printing biomimetic mechatronic systems). He chairs IEEE Norway Section and the IEEE Robotics & Automation Joint Chapter, and serves as Treasurer of NAIS (Norwegian AI Association). His research groups include Artificial Intelligence, Biomechatronics, and Collaborative Robotics. He coordinates activities in teaching robotics at bachelor/master levels and supervises PhD students. Over 70+ publications span journals like IEEE Transactions and conferences like HICSS and ICRA. Key contributions include VR/AR integration in surgical training, federated learning for edge devices, and adaptive control for flexible robotic joints. He actively promotes interdisciplinary research through digital twins, human-robot teaming frameworks, and ethical AI applications.

Jukka CoranderView profile

Professor

• Bayesian Statistics
• Inference Methods
• Stochastic Simulation Algorithms
• +7 more

Jukka Corander is a Professor at the University of Oslo's Department of Biostatistics, Faculty of Medicine, and concurrently holds positions at the University of Helsinki's Department of Mathematics and Statistics. He specializes in Bayesian statistics, microbial genomics, and evolutionary biology, with a focus on bacterial pathogen evolution and antibiotic resistance mechanisms. Affiliations: Professor, Department of Biostatistics, University of Oslo (since 2016) Professor, Department of Mathematics and Statistics, University of Helsinki (since 2009) Former roles at Åbo Akademi University and Churchill College, University of Cambridge Research Interests: Bayesian inference methods, stochastic simulation algorithms, machine learning applications in genomics, population genetics of bacteria, and forensic statistical analyses. Key Awards: 2015 Cozzarelli Prize (PNAS) for groundbreaking work on bacterial pathogen evolution 2008 Per Brahe Award (Young Scientist of the Year) Grants & Leadership: ERC StG Grant (SmartBayes, 2009–2014) COIN Centre of Excellence (Vice-Director, 2015–2017) Leadership roles in Helsinki Institute of Information Technology (HIIT) Labs & Collaborations: Leads the Corander Lab, focusing on computational methods for microbial genomics and evolutionary analysis. Collaborates globally on projects involving antibiotic resistance, pathogen transmission, and epidemiological modeling.

Franz Georg FuchsView profile

Associate Professor

• Quantum Computing
• Quantum Error Correction
• Several Complex Variables
• +3 more

Franz Georg Fuchs is an Associate Professor at the Department of Mathematics, University of Oslo. He holds a PhD in Applied Mathematics (2009) from the University of Oslo and a Master's degree in Mathematics from the Technical University of Munich (2006). His research focuses on developing quantum computing algorithms and quantum error correction, with contributions to numerical methods in fluid dynamics and magnetohydrodynamics. Education: PhD in Applied Mathematics, University of Oslo, 2009 Master's in Mathematics, Technical University of Munich, 2006 Research Interests: Quantum computing algorithms, quantum error correction, numerical methods for PDEs, computational fluid dynamics, and operator algebras. His work bridges theoretical foundations with practical applications, including software development for quantum reservoir computing and optimization frameworks like QAOA. Publications: Over 20 peer-reviewed articles in journals like Quantum , Journal of Computational Physics , and SIAM Journal on Scientific Computing . Recent work emphasizes quantum software tools and constrained optimization techniques. Awards: None explicitly listed. Active in research projects like the Gemini Center on Quantum Computing and the QOMBINE initiative. Labs/Teams: Involved in the Mathematics for Quantum Computing and Many-Body Theory (QOMBINE) project, collaborating with SINTEF and international researchers.

• Marine Systems Design
• Deep Sea Mining
• Aquaculture Logistics
• +5 more

Bjørn Egil Asbjørnslett is a Professor of Marine Systems Design at the Department of Marine Technology, Norwegian University of Science and Technology (NTNU). He serves as Director of NTNU Ocean and Coast, a strategic research area focusing on marine innovation. Additionally, he manages the Bachelor in Engineering (Aquaculture) and Minor in Aquaculture programs. His research spans deep-sea mining systems , ship design complexity , Arctic shipping , and resilient maritime logistics . He leads projects on vessel design for offshore mining, emergency response optimization, and aquaculture logistics. His recent work emphasizes technology transfer from oil/gas to deep-sea mining , multi-stakeholder design solutions , and analytical methods for vessel performance prediction . He has received no explicitly stated awards but is actively engaged in cross-disciplinary collaborations with industry and academia. His advising focuses on doctoral students in marine systems engineering and design methodologies.

Svein Olav Glesaaen NybergView profile

Associate Professor

• Bayesian Statistics
• Probability Theory
• Stochastic Processes
• +7 more

Svein Olav Glesaaen Nyberg is an Associate Professor at the Department of Engineering Sciences , University of Agder , Norway. He has been affiliated with the university since 2003, contributing to research and education in Bayesian statistics, probability theory, and stochastic processes. His work spans interdisciplinary domains including Artificial Intelligence , Biomechatronics , and Collaborative Robotics , alongside Physics and Applied Mathematics . Education: PhD (University of Oslo, 1996), MSc (University of Oslo, 1991). Prior Roles: Postdoc at University of Edinburgh (1996-98), Senior Knowledge Engineer at Computas AS (1998-2002), and teaching positions at Volda University College (2002-03). His research integrates Bayesian statistical inference with practical applications in energy systems, robotics, and materials science. Recent work includes modeling human trajectories in industrial settings using Ornstein-Uhlenbeck processes , EEG-EMG signal fusion for rehabilitation robotics, and Bayesian analysis of power grid stability . He has also published on ecological trends in Norwegian smooth snakes, linking climate change to reproductive behaviors. Scientific Awards and recognitions include authoring the textbook The Bayesian Way: Introductory Statistics for Economists and Engineers (2018) and its Norwegian counterpart Statistikk - en bayesiansk tilnærming (2016). He holds a certificate in university pedagogy (UniPed, 2009).

• Petroleum Engineering
• Drilling Engineering
• Wellbore Stability
• +12 more

Bernt Sigve Aadnøy is a Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, Faculty of Science and Technology. His work bridges theoretical and applied petroleum engineering, with a strong emphasis on drilling operations, well design, and geomechanics. His research focuses on drilling optimization, wellbore stability, drilling fluids, and smart well systems. He has extensively studied the use of nanoparticles in drilling fluids, rate of penetration modeling, torque and drag in 3D wells, and closed-loop drilling optimization. His work integrates computational modeling, laboratory experiments, and field case studies. The recent publications highlight a consistent trend in applying advanced modeling techniques—including machine learning, finite element analysis, and stochastic optimization—to solve complex drilling challenges. Topics include nanoparticle-enhanced fluids, real-time mechanical specific energy minimization, and autonomous downhole control systems, reflecting a strong interdisciplinary approach combining petroleum engineering with data science and control theory. Bernt Sigve Aadnøy has collaborated with numerous researchers and students, contributing to advancements in drilling safety, efficiency, and sustainability, particularly in challenging environments such as the Arctic and deep-water reservoirs.

Gregor DecristoforoView profile

Research Fellow

• Plasma Physics
• Fusion Energy
• Computational Physics
• +3 more

Gregor Decristoforo is a plasma physics researcher at UiT The Arctic University of Norway specializing in computational modeling of magnetically confined fusion plasmas. His work focuses on turbulence and transport phenomena in scrape-off layers, particularly studying blob dynamics and intermittent fluctuations critical for fusion energy development. His research interests span plasma turbulence modeling , computational physics , and fusion boundary layer phenomena . Using advanced numerical simulations and stochastic modeling techniques, he investigates coherent structures in magnetized plasmas, developing blob tracking algorithms and GPU-accelerated computation methods to analyze cross-field particle transport mechanisms. His publication record shows consistent research output from Master's research (2016) through doctoral work (2021) to recent first-author journal articles (2024), demonstrating ongoing contributions to understanding plasma exhaust processes in magnetic confinement devices. Current work examines RF-induced flow effects during ion cyclotron resonance heating, addressing key challenges in mitigating scrape-off layer transport for viable fusion energy production.

• Control Engineering
• Automation
• Marine Icing
• +5 more

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.

• Reverse Logistics
• Sustainable Logistics
• Closed-Loop Supply Chain
• +10 more

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.

• Computational Geoscience
• Scientific Computing
• Numerical Analysis
• +3 more

Magnus Bruaset is a Professor and Director of the Software & AI Department at Simula Research Laboratory , with expertise in computational geosciences, numerical methods, and quantum computing. His work spans geological modeling, PDE solvers, and software development frameworks like Diffpack. Education : PhD in preconditioned iterative methods for elliptic problems (1992), Master's in preconditioning symmetric systems (1988). Research Interests : Focus on numerical analysis, GPU acceleration of geological simulations, stochastic modeling for uncertainty quantification, and quantum computing strategy. Key projects include Hamilton-Jacobi solvers, particle-based flow models, and contributions to digital contact tracing systems. Advising & Collaboration : Co-developed PhD training workshops for scientific communication and contributed to industrial-academic partnerships. Collaborated with institutions including ChevronTexaco, Wolfram Research, and University of Oslo.