جست‌وجوی استاد و پژوهشگر

• Solid Mechanics
• Materials Science
• Fracture Mechanics
• +۳ مورد دیگر

Jonas Faleskog is a Professor in the Department of Materials and Structural Mechanics at KTH Royal Institute of Technology. His research focuses on mathematical modeling of material deformation and failure mechanisms, particularly in metallic and polymeric materials. Key areas include ductile and brittle fracture analysis, fracture mechanics, and computational modeling of material behavior under various stress conditions. He leads a research group collaborating internationally to develop models describing material failure at microscopic scales. Faleskog teaches courses such as Fracture Mechanics (SE2139) and Modeling in FEM (SE2860), emphasizing practical applications of theoretical models. His work spans experimental and numerical methods, addressing challenges in material heterogeneity, porosity effects, and environmental degradation. Notable contributions include advancements in weakest-link modeling for brittle failure, probabilistic fracture models, and strain gradient plasticity analysis. His research bridges material science, applied mechanics, and numerical methods to optimize material utilization in engineering systems like reactor tanks, aircraft, and vehicles. Key collaborations involve international teams exploring microstructural influences on fracture behavior. While no specific awards are listed, his extensive publication record reflects sustained contributions to mechanical and materials engineering.

Tomas Karlssonمشاهده پروفایل

استاد

• Solar Wind-Magnetosphere Interaction
• Bow Shock Physics
• Magnetosheath Jets
• +۳ مورد دیگر

Tomas Karlsson is a Professor and Deputy Head of Department at the Royal Institute of Technology , specializing in Space and Plasma Physics . He teaches courses such as EF2240 Space Physics , EF2245 Space Physics II , and EI1240 Electromagnetic Theory , while serving as examiner or coordinator for advanced projects and thesis work in space-related fields. His research focuses on the interaction between the solar wind and planetary magnetospheres , with specific interests in bow shock physics , magnetosheath jets , solar wind magnetic holes , auroral physics , and comparative studies of magnetospheres across planets and comets. He employs spacecraft data (e.g., MMS , Cluster , BepiColombo ) and simulations to analyze plasma dynamics and space weather phenomena. The 15 most recent publications highlight trends in solar wind turbulence , magnetospheric boundary processes , and planetary plasma interactions , with recurring themes in SLAMS (Short Large-Amplitude Magnetic Structures) , magnetosheath jet formation , and magnetic hole propagation . These works span statistical surveys, hybrid simulations, and multi-mission data analysis.

Giovanni Forchiniمشاهده پروفایل

استاد

• Econometrics
• Panel Data Analysis
• Health Economics
• +۳ مورد دیگر

Giovanni Forchini is a Professor at the Umeå School of Business, Economics and Statistics (USBE), Umeå University, Sweden. His research focuses on econometrics, panel data analysis, and their applications in health economics and epidemiological modeling. He holds the title of Docent, a Swedish academic qualification reflecting advanced expertise. His work bridges theoretical econometrics with practical policy analysis, particularly in pandemic preparedness and healthcare optimization. Research Themes: Econometric methodologies for panel data and structural equation models Quantifying pandemic impacts on healthcare systems and economies Optimization of resource allocation during public health crises Key Contributions: Developed the DAEDALUS model for integrated economic-epidemiological policy simulations Analyzed SARS-CoV-2 transmission dynamics and vaccine impact in multiple countries Pioneered statistical methods for handling multifactor structures in panel data Awards & Grants: USBSE Pedagogical Prize 2020 Funding from Forte (Swedish Research Council for Health, Working Life and Welfare) and Handelsbanken Teaching & Mentorship: Coordinates Master’s theses in Economics at USBSE Teaches advanced courses like Econometrics 1 & 2 and Analysis of Financial Data

• Sports Science
• Biathlon Training
• Physiological Responses
• +۷ مورد دیگر

Marko Laaksonen is a Professor at the Department of Health Sciences (HOV) at Mid Sweden University, specializing in winter sports biomechanics and performance analysis. He is affiliated with the Swedish Winter Sports Research Centre, focusing on biathlon and cross-country skiing. His research examines physiological responses to training, rifle carriage effects on skiing mechanics, and performance indicators in biathlon. Key areas include exercise intensity distribution, shooting precision, and muscle metabolism during endurance activities. Recent publications analyze biathlon shooting performance under varying conditions, training load measurement, and equipment impacts on athlete efficiency. Over 15 articles demonstrate expertise in winter sports analytics and physiological monitoring.

• Control Charts
• Measurement Error
• Performance Measure
• +۵ مورد دیگر

Hamed Sabahno serves as a Senior Lecturer in the Department of Statistics at Lund University, Sweden. His academic profile is centered on advanced statistical methodologies with applications in engineering and quality control systems. His research spans statistical process control , speckle pattern analysis , and multivariate monitoring systems . Key focus areas include adaptive control charts, measurement error correction, and image-based displacement measurement techniques. His work bridges theoretical statistics with practical industrial applications, particularly in non-destructive testing and quality engineering. Recent publications demonstrate strong emphasis on Real-time process monitoring using optical methods Multivariate regression profile control Statistical refinement in speckle-based measurements Adaptive parameter systems for quality assurance His fingerprint analysis confirms dominance in Control Charts (100%) and Measurement Error (55%) research domains. Professional engagement includes ORCID registration (0000-0002-5618-887X) and active publication in high-impact journals including Scientific Reports and Quality and Reliability Engineering International . His research has attracted attention from 5 Mendeley readers and coverage in 1 news outlet.

Måns Magnussonمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +۴ مورد دیگر

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

David Black-Schafferمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Parallel Programming
• +۴ مورد دیگر

David Black-Schaffer is a Professor at Uppsala University's Department of Information Technology, specializing in computer systems research. As of 2023, he serves as Dean of Research for the Faculty of Science and Technology. His work bridges software and hardware innovations to enhance data movement efficiency in computer systems, with applications commercialized through a startup and integrated into industry standards like OpenCL. Black-Schaffer earned his PhD in Electrical Engineering from Stanford University in 2008, focusing on many-core processor programming. His career spans roles at Apple Inc. (contributing to OpenCL standards), postdoctoral research at Uppsala University, and academic progression from assistant to full professor (2010–2017). He has held leadership roles including Head of the Division of Computer Systems (2022) and department representative on the faculty Advisory Committee for Research (2021). His research spans computer architecture, memory systems, parallel programming, and simulation techniques. Recent publications (2024–2020) explore garbage collection, cache optimization, memory contention, NUMA systems, and instruction scheduling. Key trends include software-hardware co-design for power efficiency, reuse-aware data placement, and machine learning for performance modeling. Knut & Alice Wallenberg Foundation: Wallenberg Academy Fellowship Prolongation (2020–2025), Wallenberg Academy Fellow (2016–2021) Swedish Research Council (VR): Project Grant (2019–2024), Young Researcher Grant (2015–2018), Framework Grant (2012–2017) European Research Council: ERC Starting Grant (2017–2022) Teaching Awards: Uppsala Engineering and Science Student Union Pedagogical Prize (2012), Uppsala University Pedagogical Prize (2016), Uppsala Technical Physics Students' Teaching Award (2019) Other Grants: ScalableLearning flipped classroom project (2012–2020), Arm Ltd. collaborations on memory system designs He pioneered flipped-classroom teaching through the ScalableLearning project, impacting over 80,000 students. His research is conducted in collaboration with institutions like Arm Ltd., with past contributions to Apple's OpenCL implementation and UPMARC research center.

• Outdoor Sound Propagation
• Community Noise
• Urban Acoustics
• +۵ مورد دیگر

Jens Forssén is an Assistant Professor in the Department of Technical Acoustics , Vibroacoustics research group at Chalmers University of Technology . His work focuses on outdoor sound propagation , community noise , and urban acoustics , with a particular emphasis on the influence of meteorology , terrain , and soil properties on noise levels. Research Interests: Outdoor acoustics, urban soundscapes, road and train traffic noise, wind turbine noise, noise barriers, auralization techniques, and health impacts of environmental noise. Recent Trends: Analysis of low-frequency noise in urban environments, 3D noise mapping , digital twinning for urban design, and sonic crystal applications in noise reduction. Scientific Contributions: Publications on noise propagation models , auralization methods , traffic noise assessment , and health impact studies related to urban sound environments. Labs & Collaborations: Collaborates with the LISTEN project , SONORUS project , and HOSANNA project to develop urban noise mitigation strategies. His work includes partnerships with researchers like Wolfgang Kropp , Leon Müller , and Maarten Hornikx .

• Epidemiology
• Biostatistics
• Cancer Survival Analysis
• +۳ مورد دیگر

Paul Lambert is a Visiting Professor at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, specializing in population-based cancer survival analysis and biostatistical modeling. His work focuses on flexible parametric models , competing risks , and multistate survival frameworks to address challenges in cancer prognosis and treatment effects. Key research areas: Epidemiology , Biostatistics , Cancer Survival Affiliation: Karolinska Institutet, Sweden His recent publications analyze Nordic cancer trends (2002-2021), cardio-oncology risks , and survival extrapolation in clinical trials. He supervises advanced statistical training and leads grants from the Swedish Research Council and Swedish Cancer Society. Scientific Awards : No explicit honors listed. Grants include projects on statistical methods for cancer registry data (Swedish Research Council, Swedish Cancer Society).

Matthew Hayesمشاهده پروفایل

دانشیار

• Galaxy Formation and Evolution
• Cosmic Reionization
• Interstellar Medium (ISM)
• +۴ مورد دیگر

Matthew Hayes is an Associate Professor in Astrophysics at Stockholm University 's Department of Astronomy and a Wallenberg Academy Fellow. His research focuses on the formation and evolution of galaxies, particularly during the reionization epoch, using multi-wavelength observations (JWST, Hubble, ESO) and computational modeling. Research Pillars: Cosmic reionization, Lyman-alpha emission physics, stellar feedback mechanisms, circumgalactic medium properties, and diagnostics of ionizing radiation Key Instruments: James Webb Space Telescope, Hubble Space Telescope, VLT/MUSE, Nordic Optical Telescope His recent work uses UV spectral diagnostics to trace the ionization state of the intergalactic medium and constrain the escape fraction of Lyman continuum photons. Current teaching includes graduate-level courses on the Physics of the Interstellar Medium (AS7001) and Observational Techniques in Astrophysics II (AS7004) featuring hands-on radio/optical observations. Scientific Contributions: Developed novel methods for measuring ionized bubble sizes around high-redshift galaxies Advanced understanding of Lyman-alpha as a probe for galactic winds and outflows Calibrated UV diagnostic tools for metallicity and ionization parameters in the JWST era Funding Sources: Swedish Research Council, Knut and Alice Wallenberg Foundation, Swedish National Space Board

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.

• Computer Networking
• Low Latency Communication
• Multi-Path Systems
• +۳ مورد دیگر

Johan Garcia is an Associate Professor of Computer Science at Karlstad University, Sweden, where he earned his Ph.D. in 2005. His research focuses on computer networking, particularly low-latency communication in multi-path systems, Starlink satellite performance, and deterministic network emulation using tools like KauNetEm. He has led projects such as the 8-year DRIVE initiative and the HITS project, collaborating with industry partners like Icomera. His work includes evaluating cellular and satellite connectivity for trains, as well as machine learning explainability and network protocol optimization. Education: Ph.D. in Computer Science (Karlstad University, 2005). Key projects include KauNetEm development, mobility testing across the Arctic Circle for Starlink/5G, and sectorization-based localization algorithms for eNodeBs. His research spans wireless networking, protocol evaluation, and algorithmic optimization for network reliability and performance. Research interests include: satellite communication performance, cellular networks in high-speed mobility, deterministic network emulation, ML model interpretability, and protocol efficiency in heterogeneous networks. He has advised multiple master’s and bachelor’s projects and contributed to over 100 publications since 2000. Notable collaborations include the EU FP6 Newcom Network of Excellence and the Swedish Knowledge Foundation-funded 'Reliable and Secure Communication in SCTP' project. Current roles include sub-project manager in DRIVE and co-leader of wireless network modeling work packages.

• Smart Manufacturing
• Digital Twins
• Geometry Assurance
• +۴ مورد دیگر

Dr. Roham Sadeghi Tabar is a researcher at Chalmers University of Technology , specializing in Geometry Assurance and Robust Design within the Department of Product and Production Development. His work bridges academia and industry through collaborations with automotive and aerospace sectors, focusing on optimizing variation simulation for non-rigid assemblies . Affiliation : Chalmers University of Technology Research Area : Geometry Assurance, Digital Twins, Smart Manufacturing Research Interests : Roham’s research spans Smart Manufacturing , Digital Twins , and Modeling and Simulation , with a technical focus on non-rigid assembly optimization . His work develops physics-informed AI methods to enhance geometric precision in automotive and aerospace production, addressing challenges in variation simulation and contact modeling . Publication Trends : His recent articles emphasize digital twin infrastructure for manufacturing, reinforcement learning in disassembly planning, and physics-based simulation for non-rigid parts. Key themes include spot welding sequence optimization , fixture layout , and stochastic batch assembly improvements. Professional Affiliations : Research Affiliate at CIRP, Member of ASME Projects : DigiSync, AIDCAM, PIADT, AIDDT, Digital Quality Assurance, Smart Assembly 4.0 Funding : VINNOVA, Swedish Foundation for Strategic Research (SSF) Labs & Teams : He works within the Geometry Assurance and Robust Design group, contributing to multidisciplinary projects like Digital Twin-Based Clamping Sequence Analysis and Physics-Informed AI for Assembly .

• Nonlinear Optimization
• Stochastic Optimization
• Operations Research
• +۵ مورد دیگر

Michael Patriksson is a Professor of Applied Mathematics at Chalmers University of Technology, specializing in nonlinear optimization, stochastic programming, and maintenance scheduling. His research integrates theoretical advancements with applications in industrial engineering, energy systems, and transportation. He has contributed to over 160 publications and led 10 major projects, focusing on topics such as wind turbine maintenance, supply chain optimization, and blockchain-based transportation systems. Key research interests include combinatorial optimization, variational inequalities, and algorithm design for real-world problems. Notable applied work spans predictive maintenance strategies, traffic equilibrium modeling, and resource allocation in aerospace manufacturing. His methods often address stochastic uncertainties and operational constraints, with practical implementations in industries like renewable energy and logistics. Publications highlight contributions to maintenance optimization frameworks, hybrid machine-learning algorithms, and strategic traffic management. Collaborations with institutions like CIGRE and IEEE underscore his interdisciplinary impact. Current projects explore opportunistic maintenance models, blockchain applications in mobility, and sustainable infrastructure planning.

• Numerical Linear Algebra
• Data Assimilation
• Covariance Matrices
• +۳ مورد دیگر

Jemima M. Tabeart is an Assistant Professor at TU Eindhoven, affiliated with the Centre for Analysis, Scientific Computing and Applications . Her research focuses on numerical linear algebra, data assimilation, and covariance matrices. She actively contributes to outreach and scientific crafting, and advises students on final projects in computational science. Education: PhD (2016–2019): University of Reading (supervised by Prof. Sarah Dance and others) MRes (2015–2016): Mathematics of Planet Earth CDT (Imperial College London/Reading) MMath (2011–2015): University of Bath with study abroad at Université Grenoble Alpes Research Interests: Large-scale numerical linear algebra and its applications Development of efficient preconditioners for 4D-Var and data assimilation systems Treatment of covariance matrices in computational models Interdisciplinary applications in environmental and meteorological modeling Teaching & Leadership: Organizing a 2025 Research Semester Programme at CWI on numerical analysis and machine learning Teaching courses on calculus, linear algebra, and optimization at TU Eindhoven Co-lecturing the MasterMath course on Advanced Numerical Linear Algebra Professional Activities: Member of the 4TU.AMI SRI in Bridging Numerical Analysis and Machine Learning Principal investigator in a network funded by the INI Network Support for Mathematical Sciences Advocate for low-carbon travel, documenting her journeys on her blog