Find Academic Supervisors

• Scientific Computing
• Applied mathematics
• Numerical methods
• +2 more

Marie E. Rognes is a Chief Research Scientist at Simula Research Laboratory's Numerical Analysis and Scientific Computing department. She specializes in computational mathematics and biomedical modeling, particularly focusing on cerebral fluid dynamics, electrodiffusion, and poroelasticity. Her work bridges advanced numerical methods with clinical applications in neuroscience. Research Pillars: Brain waterscape modeling, finite element methods, and biophysical simulations Software Leadership: Key contributor to FEniCS and Dolfin-adjoint projects Application Domains: Neurodegenerative diseases, cardiac electrophysiology, and personalized medicine Recent publications reveal methodological innovations in perivascular flow modeling , ionic transport simulations , and multi-scale brain mechanics . Her work on glymphatic system dynamics and cardiac tissue modeling demonstrates cross-disciplinary impact. While no explicit awards are listed, her extensive publication record in top-tier computational journals (SIAM, PLOS, Nature Computational Science) and invited talks at premier conferences (SIAM, ECCOMAS, FEniCS workshops) establish her as a leading figure in biomedical computing.

• Computational Modeling
• Molecular Simulations
• Polyelectrolytes
• +7 more

Pablo Miguel Blanco Andres is a Postdoctoral Fellow at the Department of Physics, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU). He is part of the development team for ESPResSo , an open-source molecular dynamics software for coarse-grained systems, and works as a Marie Skłodowska-Curie postdoctoral fellow on the ModEMUS project, modeling nanoparticle transport under ultrasound irradiation through the extracellular matrix. Research Interests: His work focuses on pH-dependent systems, including polyelectrolytes, peptides, and proteins, using computational modeling to study fundamental properties and develop simulation software. Key areas include charge regulation mechanisms, conformational dynamics, and multivalent interactions in biopolymers. Publication Trends: Recent articles highlight advancements in coarse-grained molecular simulations, charge regulation effects in biopolymers, and software development for soft matter systems. Topics span theoretical modeling of polyelectrolyte flexibility, experimental validation of simulation methods, and applications in biomolecular encapsulation. Scientific Awards: Marie Skłodowska-Curie Postdoctoral Fellowship Software Development: Actively contributes to ESPResSo , a versatile tool for simulating soft matter systems, with specific focus on pH-dependent modeling and coarse-grained simulations. Labs & Teams: Collaborates with interdisciplinary teams at NTNU and contributes to international research projects involving computational biophysics and soft matter physics.

Per August Jarval MoenView profile

Research Fellow

• Changepoint Detection
• Anomaly Detection
• High-Dimensional Statistics
• +3 more

Per August Jarval Moen is a Doctoral Research Fellow in the Faculty of Mathematics and Natural Sciences at the University of Oslo, affiliated with the Statistics and Data Science group. His research focuses on changepoint and anomaly detection in high-dimensional data streams, with additional expertise in minimax theory, computational statistics, and high-dimensional statistics. Education: Master of Advanced Study in Mathematical Statistics, King's College, University of Cambridge (2020-2021) Bachelor's degrees in Mathematics with Informatics and Mathematics & Economics, University of Oslo (2016-2019) Research Interests: His work addresses challenging problems in real-time anomaly detection and theoretical bounds for statistical methods. He develops algorithms for sparsity-adaptive changepoint estimation and contributes to likelihood-free computational frameworks for binary data analysis. Publications Trends: Recent research spans theoretical advancements in minimax rates, practical methodologies for high-dimensional changepoint detection, and open-source software implementations (e.g., R integration with C). Scientific Awards: Aker Scholarship (2020) Krafthack 2022 Winner (with Martin Tveten) Teaching & Supervision: He serves as a teaching assistant for STK4900 and STK3100/STK4100, and co-supervises Han Yu's master's project on hydropower sensor data modeling.

• Fluid Dynamics
• Computational Fluid Dynamics
• Turbulence
• +3 more

Jørgen Røysland Aarnes is a Research Fellow at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). His research spans fluid dynamics, computational methods, and philosophy of science, with particular focus on turbulence, multiphase flows, and structural realism in Ernst Cassirer's philosophy. He completed his PhD in 2018 with a thesis on particle-laden flows impinging on cylinders. Research Interests: Computational fluid dynamics (CFD), including high-order methods and overset grids Turbulent flows and free-surface vortex structures Particle-laden flow dynamics and impaction mechanisms Philosophy of science, structural realism, and symbolic forms Recent Contributions: His 2025 work on free-surface vortex patterns and 2024 philosophical papers on Cassirer's symbolic forms demonstrate interdisciplinary expertise. He has developed computational tools like the Pencil Code for fluid simulations. Awards: No scientific awards explicitly listed, though his work has been presented at major conferences including the European Turbulence Conference and European Geosciences Union meetings. Advising & Grants: No explicit grants or advisees listed, though his PhD supervision experience is implied through his postdoctoral role. Labs/Teams: Collaborates with the Pencil Code Collaboration and NTNU's energy engineering research groups.

• Robotics
• Artificial Intelligence
• Evolutionary Computing
• +12 more

Kyrre Harald Glette is a Professor in the Department for Informatics at the University of Oslo , affiliated with the Robotics and Intelligent Systems Research Group and the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion . His research focuses on the co-design of robot morphology and control using evolutionary and bio-inspired artificial intelligence methods. Research Interests: Robotics and Intelligent Systems Evolutionary Robotics and Co-Design of Morphology and Control Artificial Intelligence and Machine Learning Evolvable Hardware and Embodied Computation Computational Creativity and Music-AI Interaction Human-Robot Interaction and Swarm Robotics His recent publications (2023–2025) reflect a strong interdisciplinary trend, combining robotics, evolutionary computation, and music technology. Key themes include evolutionary design of modular robots , real-time adaptation in bio-inspired systems , interactive sonification , and AI-driven music generation . His work often involves the DyRET robotic platform, emphasizing real-world evolution and embodied learning. Scientific Contributions: Extensive publications in top venues like Frontiers in Robotics and AI , IEEE ICRA , GECCO , and Nature Machine Intelligence . Development of open-source robotic platforms and frameworks for evolutionary robotics. Interdisciplinary work bridging AI, neuroscience, and music. Advising and Projects: He supervises MSc students in evolutionary robotics, robot locomotion, and musical robot swarms. He is involved in several research projects including Predictive and Intuitive Robot Companion (PIRC) , Modeling and Robots , Multimodal Elderly Care Systems (MECS) , Musical Human-Machine Interaction , and Neurophysiological Mechanisms of Human Auditory Predictions (AudioPred) . Labs and Research Groups: He is a key member of the Robotics and Intelligent Systems (ROBIN) group, the RITMO Centre of Excellence , and the Creative Computing Hub Oslo (C2HO) . These groups support interdisciplinary research in AI, robotics, music, and human interaction.

Nicolaas Ervik GroeneboomView profile

Researcher

• Computational Cosmology
• Neuroimaging Utilities
• Astrophysics
• +8 more

Nicolaas Ervik Groeneboom is a researcher affiliated with the University of Oslo's Department of Astrophysics within the Faculty of Mathematics and Natural Sciences. He holds a Ph.D. in astrophysics (2010) and has conducted post-doctoral research across multiple institutions. His work bridges computational astrophysics with neuroimaging tools, particularly through the Nutil software for rodent brain data analysis and the TRSE open-source project. Education: Ph.D. in Astrophysics, University of Oslo (2010) M.Sc. in Theoretical Astrophysics, University of Oslo (2007) B.Sc. in Mathematics, University of Oslo (2006) Research focuses on: Cosmological simulations (N-body, galaxy formation) CMB anisotropies and gravitational lensing Software development for scientific visualization (OpenGL, Unity) Compiler optimizations and HPC techniques Recent work extends into neuroimaging analysis tools for Alzheimer's research. His publications span leading journals like Astrophysical Journal and Communications Biology . Grants: Recipient of a 3-year Research Council of Norway grant for his doctoral research. Active contributor to Euclid project (cosmology). Projects: Creator of open-source TRSE compiler (1000+ users) and Nutil toolbox for histological image processing.

• Sex Differences
• Gender Differences
• Human Social Behaviour
• +7 more

Bobby May is a Lecturer in the School of Psychology and Neuroscience at the University of St Andrews, affiliated with the Centre for Research into Equality, Diversity & Inclusion and the Centre for Social Learning & Cognitive Evolution. He is actively engaged in research and teaching, focusing on sex differences in human social behavior. His research interests center on sex differences in risky behavior, aggression, and emotional dynamics in social contexts. He employs meta-analytic and experimental methods to investigate how these differences manifest and evolve. A current focus is on how psychological sex differences are perceived and communicated in the media. His work aligns with UN Sustainable Development Goals, particularly gender equality. His recent publications (2023–2024) emphasize meta-analytic studies on aggression, heart rate variability, and emotional conflict in relationships. He has contributed to datasets and software tools supporting open science. His research often intersects with behavioral neuroscience, social psychology, and evolutionary perspectives. Bobby May has received no individual scientific awards listed in the text, though his collaborators have been recognized for public engagement. He is involved in advising students, as seen through his supervision of datasets and student projects. He is a Co-Investigator on two recent projects: one on digital skills development in response to COVID-19, and another on building a database for domestic abuse services. These reflect his engagement with applied research and community impact. He also participates in public science events such as EXPLORATHON and the Edinburgh Fringe, promoting science communication. Bobby May supervises research students and contributes to collaborative research teams, particularly with researchers like Donaldson, Cross, and McCurry. He supports open science through data and software sharing via repositories like Zenodo and the University of St Andrews data archive.

• Bayesian Statistics
• Quantitative Genetics
• Animal Behavior
• +5 more

Stefanie Muff is a Professor in the Department of Mathematical Sciences at NTNU, specializing in Bayesian statistical methods, quantitative genetics, and ecological modeling. Her work bridges statistical methodology with applications in evolutionary biology and wildlife research. Expertise: Bayesian inference, missing data modeling, genomic prediction, and habitat selection analysis Key affiliations: NTNU's Department of Mathematical Sciences Research interests focus on developing statistical frameworks for ecological and evolutionary questions, including studies on animal movement patterns, inbreeding effects in wild populations, and genomic prediction in non-model organisms. She actively contributes to methodological advancements in handling missing data and measurement error in ecological datasets. Recent work emphasizes applying Bayesian models to real-world ecological problems, such as dispersal genetics in vertebrate metapopulations and spatial variation in metabolic traits. Her interdisciplinary approach integrates statistics with empirical studies on birds and other wildlife. Notable collaborations: Alpine ibex conservation, house sparrow metapopulation studies, and genomic prediction frameworks Advocates for transparent science through teaching courses like ISTT1003 - Statistics and TMA4268 - Statistical Learning. Active in academic outreach with documentaries and workshops on ecological data analysis.

Sondre Sæther BollandView profile

Researcher

• Computer Science Education
• Programming Instruction
• Prior Knowledge Assessment
• +7 more

Sondre Sæther Bolland is a PhD student (Stipendiat) at the Department of Informatics, University of Bergen, Faculty of Mathematics and Natural Sciences. His work is centered on improving programming instruction in higher education through empirical research and educational innovation. His research interests focus on computer science education , particularly how students' prior programming knowledge and sense of belonging influence learning outcomes. He investigates student effort, attrition, performance, and engagement within the context of introductory computer science courses. His work bridges educational theory, psychology, and computing pedagogy. The recent publications reflect a strong trend in introductory programming education , with emphasis on curriculum design (CS1/CS2), assessment of prior knowledge, grading policies, and psychosocial factors. Projects like the Nordic Prior Knowledge Test and PIKA demonstrate a commitment to large-scale, evidence-based improvements in teaching practices. Scientific Awards : No awards are mentioned in the provided texts. Sondre has been actively involved in teaching, serving as a course instructor for Object-Oriented Programming and a teaching assistant for Algorithms, Data Structures and Programming and Object-Oriented Programming . He has also been a group leader for several foundational courses. His research is supported by funding from the Norwegian Directorate for Higher Education and Competence (HK-dir), particularly through the SFU project. He supervises no students listed, but leads collaborative research projects involving multiple institutions. He leads the PIKA (Prior Informatics Knowledge Assessment) project, an international effort to map incoming students' programming knowledge. He is also involved in developing research tools such as the Learning Space Expert Query Tool and contributes to open-source software on GitHub. His work is conducted within the Didactics research group at the Department of Informatics.

• Quantum Chemistry
• Computational Chemistry
• Solvation Modeling
• +3 more

Roberto Di Remigio Eikas is an Associate Professor in the Department of Chemistry at UiT The Arctic University of Norway. His research focuses on quantum chemistry, computational methods, and solvation modeling. Key research areas: Quantum Chemistry, Relativistic Electronic Structure Theory, Multiwavelet Frameworks, and Solvation Modeling Major contributions to open-source software development (VAMPyR, PCMSolver, MRChem) Collaborator in Nordic Quantum Chemistry Library Ecosystem His recent work explores multiwavelet-based quantum chemical methods, relativistic effects in solution-phase calculations, and stochastic approaches to coupled cluster theory. Publications span high-impact journals like Journal of Chemical Theory and Computation and Journal of Chemical Physics , highlighting innovative numerical techniques and software implementations.

Fabian BarrasView profile

Research Fellow

• Earthquake Dynamics
• Fault Mechanics
• Computational Geophysics
• +4 more

Fabian Barras is a Research Fellow at the NJORD Centre for Studies of the Physics of the Earth within the Department of Geosciences, Faculty of Mathematics and Natural Sciences, University of Oslo. His research focuses on the fundamental physics of earthquake dynamics and fault mechanics through computational modeling and theoretical analysis. His primary research interests span Earthquake Dynamics , Fault Mechanics , and Computational Geophysics , with specific expertise in frictional rupture propagation, slip pulses, and the transition from laboratory to natural earthquake scales. Barras develops and utilizes advanced numerical tools including the Akantu finite-element library and cRacklet boundary integral method for simulating dynamic fracture processes. Analysis of his 15 most recent publications reveals a strong emphasis on minimal modeling approaches to understand fundamental rupture phenomena, with particular focus on shear localization, stress drops, and the crack-like behavior of frictional ruptures. His work bridges theoretical physics with geophysical applications, frequently appearing in high-impact journals including Nature Communications, Physical Review journals, and Journal of Geophysical Research. Barras actively collaborates with leading researchers across international institutions including École Normale Supérieure (Paris), Hebrew University of Jerusalem, and ETH Zurich. His work is associated with major research initiatives including the ERC BREAK project, MODIFLOW, and UNLOC. His technical contributions include significant software development for high-performance computational geophysics, particularly through the open-source Akantu and cRacklet libraries that enable advanced simulation of contact mechanics and dynamic fracture phenomena at multiple scales.

• Software Engineering
• Digital Fabrication
• Computer-Aided Design (CAD)
• +2 more

Frikk Fossdal is a Research Fellow at the Department of Computing, Mathematics, and Physics, working on software engineering and digital fabrication technologies. His office is located in room E506. Research Focus: Development of frameworks and tools for digital fabrication workflows, parametric modeling, and CAD-integrated machine control. Key Contributions: Creation of the Vespidae programming framework, Fabricatable toolkit, and models for customized fabrication machines. Scientific Trends: Combines software engineering principles with mechanical design to enable interactive control of fabrication machines within CAD environments, emphasizing modularity and parametric customization.

Duncan WattsView profile

Researcher

• Cosmology
• Cosmic Microwave Background (CMB)
• Astrophysics
• +3 more

Duncan Watts is a Researcher at the University of Oslo’s Department of Astrophysics within the Faculty of Mathematics and Natural Sciences. He holds a PhD in Physics and Astronomy from Johns Hopkins University (2018) and a BA in Physics and Astronomy from Harvard College (2012). His primary research focuses on cosmology, the cosmic microwave background (CMB), and analyzing microwave emissions from the Milky Way to address fundamental questions about the universe. Watts is a key contributor to the Cosmoglobe project and the CMB&CO group, advancing Bayesian statistical methods for CMB analysis and end-to-end error propagation. He has also worked on the Cosmology Large Angular Scale Surveyor (CLASS) experiment, focusing on instrument calibration, polarization measurements, and foreground separation. His affiliations include the University of Oslo (2020–present) and Johns Hopkins University (2018–2020). Research interests span cosmological parameter estimation, CMB polarization, instrument design, and open science practices. Notable projects include reprocessing WMAP/Planck data for synchrotron emission models and constraining cosmic birefringence. Watts collaborates on initiatives like BeyondPlanck and LiteBIRD, emphasizing reproducible methodologies and data longevity. Publications emphasize CMB instrumentation, data pipelines, and Bayesian analysis, with recent works addressing atmospheric cloud polarization, bolometer performance optimization, and full-sky synchrotron modeling. His work bridges observational cosmology with computational techniques, aiming to refine CMB data interpretation and cosmological inference. Labs/teams: COSMOGLOBE, CMB&CO group, BeyondPlanck collaboration, and the COMAP project. Active in developing tools like ZodiPy for simulating zodiacal emission and improving CMB mapmaking algorithms through Gibbs sampling.

Anders KvellestadView profile

Researcher

• Particle Physics
• Theoretical Physics
• High-Energy Physics
• +5 more

Anders Kvellestad is a Researcher and Lecturer in Theoretical Physics at the University of Oslo. His work focuses on extensions of the Standard Model, particularly supersymmetry and theories with additional Higgs bosons, and their experimental testing at the Large Hadron Collider (LHC). He leads the development of the GAMBIT software tool, which facilitates global statistical analyses of beyond-Standard-Model theories. His research also encompasses statistical methods (Bayesian and frequentist) and machine learning applications in particle physics, including fast simulations and data analysis. Kvellestad has held postdoctoral and research roles at institutions such as Imperial College London and the Nordic Institute for Theoretical Physics (Nordita). He contributes to both academic teaching and public outreach, delivering lectures on computational physics and Bayesian statistics, and engaging in science communication through podcasts and media. His academic journey includes a PhD from the University of Oslo (2015), an M.Sc. from the University of Bergen (2011), and a B.Sc. from the University of Bergen and University of Copenhagen (2009). He is a core developer of the GAMBIT and xsec software projects, which are open-source tools used globally for particle physics research. His work bridges theoretical and computational physics, emphasizing interdisciplinary collaboration across particle theory, statistics, and scientific computing. Key projects include optimizing BSM physics searches at the LHC, interpreting LHC results for new physics, and leveraging machine learning for fast cross-section evaluations. His contributions to software development, such as the xsec tool for rapid LHC cross-section analysis, highlight his focus on computational efficiency and reproducibility in high-energy physics research.

Isak RoalkvamView profile

Senior Lecturer

• Stone Age
• settlement patterns
• quantitative methods
• +3 more

Isak Roalkvam is a Senior Lecturer in Archaeology at the University of Oslo, specializing in Stone Age studies with a focus on quantitative methods and computational approaches. His research integrates geographical information systems, programming (R and Python), and open research practices to investigate settlement patterns along the Norwegian coast during the Mesolithic period, particularly addressing challenges posed by relative sea-level changes over millennia. Dr. Roalkvam's educational background includes: PhD in Archaeology (2024) from the University of Oslo MSc in Computational Archaeology: GIS, Data Science and Complexity (2019) from University College London Master's degree in Archaeology (2015) from the University of Oslo Roalkvam's research centers on the application of quantitative methods to understand Stone Age settlement patterns, particularly along the Norwegian Skagerrak coast. He specializes in shoreline dating techniques, summed probability distributions of radiocarbon dates, and the relationship between Stone Age sites and sea-level changes. His technical expertise includes developing computational tools (such as the shoredate R package), statistical modeling, and GIS analysis. He is also actively engaged in promoting open research practices within archaeology, as evidenced by his recent publications on open access publishing in the field. Analysis of Roalkvam's publication record reveals a clear methodological trajectory from theoretical development to practical application. His work consistently addresses the complex relationship between archaeological site locations and relative sea-level changes, requiring sophisticated computational approaches. The development of the shoredate R package represents a significant contribution to the field, providing researchers with accessible tools for shoreline dating. More recently, his research has expanded to examine broader issues in archaeological publishing and open science practices, demonstrating his commitment to advancing methodological rigor and transparency in the discipline. Dr. Roalkvam is actively involved in two research groups at the University of Oslo: Materialities – past and present PalaeoTECH – Social approaches to Stone Age technology