جست‌وجوی استاد و پژوهشگر

• Wildlife Ecology
• Large Mammal Conservation
• Human-Wildlife Conflict
• +۵ مورد دیگر

Martin Mayer serves as Associate Professor at the University of Inland Norway within the Faculty of Applied Ecology, Agricultural Sciences and Biotechnology and the Department of Forestry and Outback Studies. Based at the Evenstad study location, his research focuses on large mammal ecology in human-dominated landscapes across Scandinavia and Europe. His primary research interests center on Wildlife Ecology and Large Mammal Conservation , with particular emphasis on human-wildlife conflict resolution, road ecology, and carnivore management. Mayer investigates how species like moose, deer, and beavers adapt to anthropogenic landscapes through habitat selection, movement patterns, and population dynamics. His work integrates cutting-edge methodologies including GPS telemetry, drone surveillance, and citizen science data to address conservation challenges in agricultural and urban settings. Analysis of Mayer's recent publications reveals strong trends in using roadkill data for population monitoring, examining spatial constraints on wildlife movement, and developing mitigation strategies for human-wildlife conflicts. His research increasingly incorporates cross-border European perspectives on wildlife management while maintaining a strong foundation in Scandinavian field ecology. Mayer actively contributes to the LARGE research group (ecology of large animals), which conducts field studies on ungulate behavior, predator-prey dynamics, and landscape-scale conservation planning in Norway's boreal ecosystems.

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +۷ مورد دیگر

Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.

• Mining Rock Mechanics
• Rock Mass Properties
• Underground Support Systems
• +۲ مورد دیگر

Hakan Basarir is a Professor in the Department of Mining Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. His research and teaching focus on mining rock mechanics, rock mass characterization, underground support systems, and the application of soft computing methods in mining engineering. PhD in Mining Engineering (2002) 20+ years of research and teaching experience 60+ publications in journals and conferences Research Interests include rock mass property prediction using measurement while drilling (MWD) techniques, numerical modeling of mining structures, optimization of mine support systems, and sustainable material development. His work integrates machine learning and computational methods to address challenges in mining geomechanics and backfill design. Recent Publications highlight advancements in AI-driven lithology prediction, eco-concrete formulation, and backfill mixture optimization. He has also contributed to tunnel stability analysis and seismic rock slope modeling. Teaching includes advanced courses in mining engineering, mineral production modeling, and specialization projects in geotechnology.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +۴ مورد دیگر

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

Arnoldo Frigessiمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Machine Learning
• +۵ مورد دیگر

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

Sverre A. C. Kittelsenمشاهده پروفایل

استاد

• Production theory
• Efficiency measurement
• Regulation
• +۲ مورد دیگر

Sverre A. C. Kittelsen is a Senior Research Fellow at the Frisch Centre and holds a Professor II position at the Department of Health Management and Health Economics (HELED) within the Institute of Health and Society at the University of Oslo. His academic career spans over three decades with a Doctor Politicae (Dr.Polit.) in Economics from the University of Oslo (1998) and a Cand.Polit. in Economics from the University of Bergen (1985). His primary research interests include Production theory, Efficiency measurement, Regulation, Health Economics, and Energy Economics. Kittelsen has established himself as a leading researcher in Nordic healthcare efficiency analysis, particularly focusing on hospital productivity, quality assessment, and the economic implications of healthcare reforms across Scandinavian countries. He has also made significant contributions to energy economics, especially regarding European energy markets and climate policy. His recent publication record demonstrates remarkable productivity with 15+ publications since 2017, showing consistent scholarly output across health economics and energy economics. The research trends indicate a strong focus on empirical analysis of healthcare systems using advanced econometric methods, particularly data envelopment analysis (DEA) and productivity measurement techniques. His work frequently involves cross-national comparative studies within the Nordic region, examining how different healthcare organizational structures affect efficiency and quality outcomes. Kittelsen has been involved in numerous research projects related to public sector efficiency, including hospital productivity studies, tax office efficiency analysis, and energy market modeling. His work often bridges theoretical economic concepts with practical policy implications for healthcare and energy sectors. His institutional affiliations with both the Frisch Centre (a prominent Norwegian economic research institute) and the University of Oslo position him at the nexus of academic research and policy-relevant analysis. This dual affiliation enables him to contribute both to theoretical economic literature and to practical policy discussions in Norway and the broader Nordic region.

Tønnes Nygaardمشاهده پروفایل

دانشیار

• Robotics
• Evolutionary Algorithms
• Morphological Adaptation
• +۳ مورد دیگر

Tønnes Nygaard is an Associate Professor at the Department of Technology Systems, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on evolutionary robotics, morphological adaptation, and embodied artificial intelligence. He leads projects like COCOMO (Co-evolution of Control and Morphologies) and works extensively with the DyRET (Dynamic Robot for Embodied Testing) platform. Key research interests include robot control systems, adaptive morphology design, and real-world implementation of evolutionary algorithms. His work bridges theoretical computer science with practical robotics applications, emphasizing hardware-software co-evolution and embodied cognition principles. Publications span topics like morphological adaptation in quadruped robots, semi-supervised learning for terrain classification, and overcoming convergence issues in multi-objective evolutionary algorithms. Nygaard collaborates internationally and contributes to both academic journals and conferences in robotics and AI. No scientific awards are explicitly listed, though his impactful contributions to real-world evolutionary robotics suggest potential recognition pending explicit mentions. Advising and grant activities are central to his role, though specific student names or grant amounts are not detailed in the provided texts. Labs/Teams: Core contributor to the DyRET project and affiliated with the Section for Autonomous Systems and Sensor Technologies at UiO.

Lars Moberg Salbuمشاهده پروفایل

پژوهشگر ارشد

• Computational Geometry
• Algebraic Topology
• Parameterized Complexity
• +۳ مورد دیگر

Lars Moberg Salbu is a Postdoctoral Fellow at the Department of Informatics, University of Bergen. His research focuses on computational geometry, algebraic topology, and parameterized complexity, with contributions to graph theory, metric analysis, and algorithm design. He has collaborated with institutions such as the Department of Mathematics and Department of Computer Technology at Western Norway University of Applied Sciences. His work bridges theoretical mathematics and computer science, addressing challenges in topological data analysis and combinatorial optimization. Key publications include studies on transition graph dynamics, minimum bounded chains, and homology determination from data samples. Salbu’s research emphasizes interdisciplinary approaches, leveraging discrete mathematics and geometric principles to solve complex computational problems. No scientific awards are listed, but his active participation in international journals like IEEE Access and Mediterranean Journal of Mathematics highlights his academic contributions. Collaborations with co-authors such as Morten Brun and Belen Garcia Pascual underscore his collaborative approach to advancing theoretical frameworks in his field.

Luca Fredianiمشاهده پروفایل

استاد

• Theoretical and Computational Chemistry
• Density Functional Theory (DFT)
• Multiwavelet Methods
• +۴ مورد دیگر

Luca Frediani is a Professor in Theoretical and Computational Chemistry at the Hylleraas Center, Department of Chemistry, UiT The Arctic University of Norway. His research focuses on advanced quantum chemistry methods, including density functional theory, multiwavelet basis sets, and solvation modeling. He actively develops computational tools like MRChem and VAMPyR for molecular electronic structure calculations. Current affiliation: UiT The Arctic University of Norway Research group: Theoretical and Computational Chemistry Teaching: KJE-2001 Theoretical Chemistry and Spectroscopy His work spans relativistic quantum chemistry, numerical methods for response properties, and benchmarking of basis set limits. Publications emphasize eliminating basis set errors, multiwavelet applications, and polarizable continuum models for solvation. He collaborates extensively on software development for quantum chemistry. Recent articles highlight multiwavelet-based DFT at the basis set limit, noise-tolerant force calculations, and relativistic effects in electronic structure. Sub-fields include scalar relativity, cavity-free solvation, and metal-ligand interaction accuracy.

• Marine Renewable Energy
• Wind Energy Systems
• Drivetrain Design and Reliability
• +۷ مورد دیگر

Amir R. Nejad is a Professor in the Department of Marine Technology at NTNU, leading the Marine Energy Systems and Autonomics (MESA) research group. He holds roles such as Chair of the EAWE WindEurope Scientific Track Committee and co-chair of the Drivetrain Technical Committee at the European Academy of Wind Energy (EAWE). His research focuses on stochastic design, reliability-based operation, dynamic modeling, and fault detection in marine and offshore renewable systems. Nejad is an editorial board member for journals like Wind Energy Science and Ocean Engineering , and a member of ISO committees on drivetrain health monitoring. Education: PhD in Marine Technology, NTNU (2015) MSc Subsea Engineering, University of Aberdeen (2012) BSc Mechanical Engineering, Tehran University (2009) His research interests emphasize offshore wind drivetrain reliability, condition monitoring, and digital twin applications. Recent work includes studies on blade bearing fatigue estimation, SCADA-based lifetime extension of drivetrains, and wake steering techniques in floating wind farms. Key awards include multiple 'Best Lecturer' honors at NTNU and international conference recognitions. His research has been supported by grants such as the Nowitech Fellowship and DNV Education Fund. Key Projects: Marine System Dynamics and Vibration Lab (MD Lab) oversees projects on wind turbine drivetrains, floating offshore systems, and digital twin integration. Lab/Teams: MD Lab focuses on advancing marine energy systems through experimental and computational studies.

• Mathematical Modeling
• Simulation of Machinery Systems
• Fluid Dynamics
• +۶ مورد دیگر

Eilif Pedersen is a Professor and Program Leader for Marine Technology at the Norwegian University of Science and Technology (NTNU). He leads the Department of Marine Technology under the Faculty of Engineering. His research focuses on mathematical modeling, simulation of machinery systems, and energy-efficient solutions in marine and offshore contexts. He is actively involved in projects such as SEACo, SFI Smart Maritime, and ViProMa, emphasizing virtual prototyping and hybrid power systems. Key research areas include bond graph methodology, thermodynamic system modeling, and dynamic analysis of marine systems. Pedersen supervises numerous PhD and industrial projects, including studies on hybrid propulsion, wind turbine dynamics, and fuel cell integration. He has contributed to over 50 publications, with recent work addressing co-simulation techniques, energy conservation in marine systems, and emission reduction strategies. His expertise spans marine engines, fluid dynamics, and renewable energy applications. Collaborations with industry partners like Rolls-Royce and Kongsberg Digital highlight his commitment to bridging academic research with practical maritime challenges.

Sergej Stoppelمشاهده پروفایل

پژوهشگر

• Visual Data Science
• Data Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Sergej Stoppel is currently a researcher (PostDoc) in the visualization group at the University of Bergen. His academic background includes a Mathematics master's degree (2014) and a PhD in 2018, both from the University of Bergen. His research focuses on visual data science, with an emphasis on interactive visualization techniques, human-computer interaction, and physical visualization systems. He has authored/co-authored numerous papers on topics such as hexagonal map enhancements, spatio-temporal data interaction, and low-cost physical art generation. Education: M.Sc. Mathematics (2014), University of Bergen PhD in Visualization (2018), University of Bergen Research Interests: His work spans data visualization, scientific visualization, and interactive techniques. Notable contributions include Vol²velle (printable interactive volume visualization widgets), Firefly (virtual illumination drones), and LinesLab (low-cost art generation systems). He explores methods to bridge the gap between virtual and physical visual representations, emphasizing user-centric design. Publications: His recent work includes advancements in hexagonal map visualization (2022), spatio-temporal selection techniques (2020), and illumination automation (2019). These contributions highlight his focus on enhancing data interpretation through novel interaction paradigms. Labs/Teams: Active member of the VisGroup at the University of Bergen, collaborating on projects like MetaVis and VIDI. His work is supported by grants involving visualization and medical imaging applications.

Milica Orlandicمشاهده پروفایل

دانشیار

• Hyperspectral Imaging
• Remote Sensing
• Digital Design
• +۷ مورد دیگر

Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.

Jaakko Timo Henrik Järviمشاهده پروفایل

استاد

• Generic Programming
• Programming Language Design
• Graphical User Interfaces (GUI)
• +۷ مورد دیگر

Jaakko Timo Henrik Järvi is a Professor in the Department of Informatics at the University of Bergen, Norway, with additional affiliations at the University of Turku, Finland. His research focuses on programming language design, generic programming, and human-computer interaction, particularly in GUI frameworks and software reuse. His research interests include generic programming, programming language design (especially the Magnolia language), high-performance computing, array programming, and GUI engineering. He emphasizes formal methods and algebraic specifications to build reusable and efficient software systems. His work bridges theoretical foundations with practical applications in software development and education. The recent publications highlight a strong trend in declarative GUI frameworks, multi-selection models, and generic programming. His work explores domain-specific languages for GUI structure manipulation, reusable selection semantics across platforms, and optimizing array computations using the Mathematics of Arrays. These efforts reflect a consistent focus on software abstraction, correctness, and reusability. Jaakko Järvi has supervised doctoral students, including Tetiana Yarygina, whose dissertation explored microservice security. While no specific grants are detailed, his work on VisAST was supported by the Research Council of Norway (Project 250683), indicating active external funding. He frequently collaborates with researchers like Magne Haveraaen, Knut Anders Stokke, and Sean Parent. He contributes to tools and frameworks such as the MultiselectJS library and the VisAST educational tool. These are outcomes of collaborative research teams focused on improving software development practices and computer science education.

• Scanning Transmission Electron Microscopy (4D-STEM)
• Open Source Scientific Software Development
• Big Data Processing in Electron Microscopy
• +۴ مورد دیگر

Magnus Nord is an Associate Professor in the Department of Physics, Faculty of Natural Sciences at Norwegian University of Science and Technology (NTNU). His research focuses on advanced electron microscopy techniques and computational tools for materials characterization. Research Interests : Scanning Transmission Electron Microscopy (4D-STEM), Open Source Scientific Software Development (Python), Big Data Processing, Magnetic/Electric Field Imaging, Structural Characterization using Higher Order Laue Zones. Publications span cutting-edge applications in functional materials, nanomagnets, and perovskite thin films, with emphasis on machine learning and precession-enhanced imaging. Key keywords include Materials Science , Electron Microscopy , and Computational Imaging . Software Development : Lead developer of Atomap and pyxem , contributing to HyperSpy and merlin_interface for electron microscopy data analysis. Current Research Funding : InCoMa (Research Council of Norway) IMPRESS (Horizon EU Program)