Find Academic Supervisors

• Mechanical Engineering
• Materials Science
• Rheology
• +5 more

Stefan Lindström is a Professor in Mechanical Engineering at Mid Sweden University. He is affiliated with the Department of Engineering, Mathematics and Science Education (IMD) and the FSCN Research Centre. His research focuses on mechanical behavior of fibrous materials, fatigue modeling, colloidal systems, and computational mechanics. Research interests: Mechanical properties of fiber suspensions and networks High-cycle fatigue analysis in aerospace components Colloidal gels and nanocellulose materials Computational modeling of multiphase flows Topology optimization with fatigue constraints Microstructural characterization of materials Publications highlight his expertise in fatigue life prediction, fiber network simulations, and advanced materials. He has contributed to understanding: Mechanical behavior of cellulose nanofibrils Damage accumulation in composite structures Hydrogel mechanics and stimuli-responsive materials Topology optimization for anisotropic materials He has collaborated extensively with researchers like Lars Wågberg, Jonas Moverare, and Christophe Thore. His work spans applications in paper physics, aerospace engineering, and sustainable nanomaterials.

• Control Engineering
• Systems Identification
• Machine Learning
• +3 more

Håkan Hjalmarsson is a full Professor at the KTH Royal Institute of Technology , affiliated with the Division of Decision and Control Systems within the School of Electrical Engineering and Computer Science . He serves as examiner for advanced-level degree projects in Computer Science , Electrical Engineering , and Systems Engineering , while also contributing as assistant, course manager, and teacher for Control Engineering courses. Research focus on Control Theory , System Identification , and Data-Driven Control Expertise in Optimal Experiment Design , Sparse Estimation , and Bayesian Methods Active in Industrial Applications including bioprocess optimization and network control His recent publications (2024-2025) emphasize finite-time regret minimization , sparse system identification , dynamic programming exploration , and Bayesian approaches to control problems. Key subfields include linear quadratic control , Markov parameter estimation , Wiener-Hammerstein models , and metabolic network identification . He has no listed scientific awards in the provided data.

• Marine Environmental Science
• Shipping Impact Assessment
• Shipwreck Risk Assessment
• +4 more

Ida-Maja Hassellöv serves as a Full Professor at Chalmers University of Technology within the Transport, Energy and Environment division, specializing in Maritime Environmental Sciences. She has established herself as a leading expert in assessing the environmental impact of maritime activities, with particular focus on shipping's effects on marine ecosystems. Her research spans multiple critical areas of marine environmental science: Holistic assessments of shipping's environmental footprint Shipwreck risk assessment methodologies Seawater scrubbing technology and its ecological consequences Exhaust gas treatment systems and marine pollution Shipping-related contributions to ocean acidification Development of environmental impact assessment frameworks Analysis of her recent publications reveals a strong emphasis on the environmental paradoxes of shipping technologies, particularly how scrubber systems that reduce air pollution create new water pollution challenges. Her work consistently bridges marine chemistry, environmental policy, and engineering solutions with significant implications for regulatory frameworks. Hassellöv chairs the ICES Working Group on Shipping Impacts in the Marine Environment since 2020 and actively contributes to the UN World Ocean Assessments. She has secured substantial research funding from organizations including the European Commission, Formas, and the Swedish Transport Administration for projects addressing maritime environmental challenges. Her research group maintains close collaborations with governmental authorities, international organizations, and industry partners to develop evidence-based approaches for sustainable shipping practices and marine environmental protection.

Mikael NilssonView profile

Senior Lecturer

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Mikael Nilsson is a Senior Lecturer and key member of the Faculty of Engineering (LTH) at Lund University , Sweden. He is affiliated with the Centre for Mathematical Sciences and serves as Programme Director for the international master's program in Machine Learning, Systems and Control since 2019. Education : M.Sc.E.E. and Ph.D. in Applied Signal Processing (2007) from Blekinge Institute of Technology Research Focus : Developing novel Machine Learning models with emphasis on Deep Neural Network Designs Applications in Computer Vision (3D object estimation, traffic safety, radiation therapy) Applied research in Engineering Health and Natural & Artificial Cognition profile areas Recent Research Outputs (2022-2023): Medical imaging segmentation for cancer therapy (Radiation Oncology) Urban traffic surveillance systems (IEEE ITSC) 3D shape estimation from monocular vision Multi-view pose estimation via segmentation masks Yield forecasting using satellite-ground data fusion Key Collaborations : Swedish Government Agency for Innovation Systems (Vinnova) - DAIDESS project (2023-2026) Interdisciplinary work in Engineering Health and Medical Imaging Industrial collaboration with Axis Communications AB (2016-2019)

Johannes BintingerView profile

Assistant Professor

• Biochemistry
• Iontronic Devices
• Drug Delivery Systems
• +3 more

Johannes Bintinger is an Assistant Professor at Linköping University, affiliated with the Department of Science and Technology (ITN). His research focuses on integrating biochemistry and iontronic devices to achieve precise drug delivery systems, wearable sensors, and e-skin applications. He leads projects such as the EU-funded bioSWITCH initiative, aiming to develop click-chemistry-based cancer therapies using implantable ion pumps. His work emphasizes organic electronics, particularly in sensor design and material optimization. Notable projects include the development of organic field-effect transistor (OFET) sensors for deformation detection and biosensing applications. He coordinates interdisciplinary efforts in the Laboratory of Organic Electronics (LOE), advancing materials science and biomedical technologies. Bintinger has received a European Research Council (ERC) Starting Grant, recognizing his contributions to innovative sensor technologies and drug delivery systems. His research bridges fundamental material science with applied biomedical engineering, addressing challenges in precision medicine and wearable health monitoring. He collaborates extensively, as seen in publications on topics like plasmonic sensors, graphene-based biosensors, and printed polymer composites for gas detection. His work often highlights cost-effective, versatile tools for environmental and medical applications.

• Geodesy
• GPS Technology
• Atmospheric Science
• +3 more

Anna Jensen is a Professor of Geodesy at KTH Royal Institute of Technology, specializing in atmospheric effects on Global Navigation Satellite Systems (GNSS). Her research focuses on modeling ionospheric and tropospheric influences on GPS signals, particularly how weather parameters and electric currents impact signal transmission. Applications of her work include precision surveying, aviation navigation, and construction site monitoring. She has contributed to projects such as the Fehmarnbelt Fixed Link positioning system and Arctic navigation challenges. Her research interests span geodesy, satellite navigation, and remote sensing, with a strong emphasis on improving GNSS accuracy in challenging environments like urban areas and polar regions. Jensen’s studies address issues like ionospheric scintillation, RFI geolocation with smartphones, and robust positioning in jamming conditions. Jensen has published extensively on topics including network RTK data screening, GNSS network optimization, and ionospheric modeling. Her work often integrates statistical methods and geostatistical techniques to enhance positioning reliability. No scientific awards or grants are explicitly mentioned in the provided texts. She has advised no formal students, though her involvement in the Fehmarnbelt project and other large-scale infrastructure initiatives highlights her role in applied geodetic engineering. No specific labs or teams are detailed, but her collaborations likely involve KTH’s geomatics and geophysics groups.

Stina EngelheartView profile

Researcher

• Geriatric Nutrition
• Nutritional Status in Elderly
• Home Healthcare Nutrition
• +3 more

Stina Engelheart is an affiliated researcher at Örebro University's Department of Medical Sciences within the School of Medical Sciences. She holds a part-time role as a dietitian in Örebro Municipality's Health and Welfare Administration, focusing on education and development related to food, meals, and nutrition. Her doctoral thesis (defended in 2021) explored nutritional status in a functional perspective among older adults receiving home health care. Education & Academic Background: No explicit education details listed, but her research and publications indicate advanced training in nutrition science and gerontology. Research Focus: Her work centers on geriatric nutrition, nutritional status assessment in elderly populations, meal patterns, dehydration risks, and the interplay between gut health and wellbeing. Key themes include optimizing nutritional care in home healthcare settings and evaluating municipal support systems for elderly nutrition. Collaborations: Active collaborations with Örebro Municipality, particularly in dementia care environments (REDem project) and wellbeing promotion via care technologies (WellCET group). Her research also involves partnerships with institutions like Clinical Nutrition ESPEN and The Journal of Nutrition, Health & Aging. Grants & Funding: While specific grants are not listed, her involvement in multi-year projects (e.g., REDem since 2021) suggests sustained institutional or municipal funding. Her thesis research was likely supported by Örebro University. Labs/Teams: Works within the Wellbeing promotion using care environments and technologies (WellCET) research group and contributes to the Rethinking Environments in Dementia Care Homes (REDem) initiative.

• Machine Learning
• Bayesian Inference
• Deep Learning
• +5 more

Najmeh Abiri holds roles as a Research Engineer, Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), Postdoc in the Department of Statistics, and Researcher in Computational Science for Health and Environment at Lund University. She is affiliated with eSSENCE: The e-Science Collaboration. Her primary research interests include variational inference, Bayesian inference, deep learning, computational biology, and machine learning, contributing to UN Sustainable Development Goals related to environmental sustainability and health. Abiri has conducted impactful research on probabilistic multivariate time series imputation using diffusion models, disease risk mapping with AI, and quantum mechanics applications of deep learning. She has also pioneered denoising autoencoder techniques for handling missing data across diverse datasets. Notable outputs include a 2022 study predicting West Nile virus outbreaks using eco-climatic drivers and a 2021 paper on inverse problems in quantum mechanics. Her work spans interdisciplinary collaborations, addressing global challenges through data-driven approaches. Abiri organized workshops on AI/ML technologies and GAN applications, fostering academic engagement. She led the 2014-2019 project on statistical inference with deep latent variable models, underscoring her commitment to advancing computational methodologies in science and health.

Martin LindView profile

Assistant Professor

• Mathematical Analysis
• Number Theory
• Discrepancy Theory
• +4 more

Martin Lind is an Assistant Professor of Mathematical Analysis at Karlstad University. His research focuses on advanced topics in mathematical analysis, including discrepancy theory, functions of bounded variation, approximation theory, and multiscale systems. He holds a PhD from Karlstad University (2013) and completed a post-doctoral fellowship at the University of South Carolina (2014-2015). Education: PhD in Mathematics, Karlstad University, 2013 His work bridges pure and applied mathematics, addressing problems in number theory, functional analysis, and numerical methods. Key research themes include: Discrepancy estimates for pseudorandom sequences Properties of functions with bounded variation (p-variation, Lambda-variation) Nonlinear approximation methods using splines Analysis of multiscale elliptic-parabolic systems His recent publications (2020-2024) emphasize number-theoretic aspects of sequences and their applications in quasi-Monte Carlo methods. He collaborates actively on applied projects like pollution reduction modeling with photocatalytic materials. Notable contributions include: Developing variational characterizations of Sobolev spaces Establishing convergence rates for semidiscrete Galerkin schemes Exploring Fubini-type properties in multivariate analysis