Anders Västberg is a Lecturer at KTH Royal Institute of Technology within the Department of Communication Systems. His work spans teaching and examination roles across various courses in computer science, electrical engineering, and information and communication technology (ICT) innovation, including Wireless Communication Systems , Mobile Networks , and Programming of Parallel Systems . Teaches courses like Internet of Things and Introduction to Computer Security . Examiner for advanced-level degree projects in embedded systems and communication systems. Focuses on wireless networking, heterogeneous networks, and energy-efficient network design. His research interests include energy efficiency in telecommunications , green radio systems , and network optimization . Recent work explores power consumption in backhaul systems , heterogeneous network deployment , and signal propagation in ionospheric channels . Articles highlight trends in green networking , starting with 2016 studies on cell DTX and heterogeneous networks , followed by 2013 work on backhaul optimization and wideband efficiency . Earlier papers (1997–2008) focus on ionospheric signal distortion and HF channel analysis .
Professor Carolin Körner serves as Head of the Chair WTM (Materials and Technology Management) at Friedrich Alexander University Erlangen-Nuremberg's Faculty of Engineering, Department of Materials Science and Engineering. She also holds significant leadership positions as Member of the control board of the Central Institute of New Materials and Processes (ZMP) in Fürth and Scientific head of the division Additive Manufacturing at New Materials Fürth, Ltd. Her research focuses on additive manufacturing technologies, particularly electron beam powder bed fusion (EB-PBF), with emphasis on process optimization, microstructure evolution, and development of advanced metallic materials. Her work spans fundamental process understanding to industrial applications, addressing challenges in thermal management, material development, and process control for high-performance components. She has pioneered approaches for in-situ alloying, complex geometry fabrication, and microstructure tailoring through innovative scanning strategies. Analysis of her recent publication record reveals consistent leadership in electron beam additive manufacturing research, with particular emphasis on process simulation, novel material development (including high-temperature alloys, copper-based systems, and shape memory alloys), and advanced characterization techniques. Her work demonstrates strong industry relevance while maintaining rigorous scientific foundations, with applications spanning aerospace, medical devices, and energy sectors. As Head of the Chair WTM, Professor Körner leads a substantial research team and collaborates extensively with industry partners. Her leadership extends to directing research initiatives at the Central Institute of New Materials and Processes, where she oversees significant resources for advanced manufacturing development. While specific grant details aren't provided in the source material, her extensive publication record and leadership roles indicate successful acquisition of substantial research funding. Professor Körner's laboratory infrastructure is closely tied to the Central Institute of New Materials and Processes in Fürth, which houses state-of-the-art electron beam powder bed fusion systems and complementary characterization equipment. Her research group forms part of a larger collaborative ecosystem focused on advancing additive manufacturing technologies from fundamental research to industrial implementation.
Jens Wahlström is a Professor and Office Director at the Mechanics, Materials and Component Design department within Lund University's Faculty of Engineering. He is also a member of the LTH Profile Area: Aerosols. Chaired Professor in Machine Elements Excellent Teaching Practitioner (ETP) Active in interdisciplinary research projects Research Focus : Tribological performance and non-exhaust emissions from machine elements in road and rail vehicles, including brakes, gears, tyres, and wheel-rail systems. His work bridges Mechanical Engineering with Environmental Science , emphasizing sustainable solutions. Publication Trends : Recent studies analyze airborne particle emissions from mechanical systems, friction material optimization, and simulation techniques for wear prediction. These align with global goals for Clean Air and Sustainable Infrastructure . Awards & Recognition : Stanford/Elsevier Top 2% Scientists (2020–2025) in Mechanical Engineering & Transports Excellent Teaching Practitioner (ETP) at Lund University Collaborations & Projects : Leads and contributes to projects addressing brake wear particles, sustainable road materials, and nanoparticle emissions. Collaborates with European institutions and industry stakeholders.
Nicholas Morse is a Postdoctoral Researcher in the Engineering Mechanics Department at KTH Royal Institute of Technology in Stockholm, Sweden. He joined KTH in May 2025 and is supervised by Professors Philipp Schlatter (FAU Erlangen, KTH), Ramis Örlü (OsloMet, KTH), and Mihai Mihaescu (KTH). Dr. Morse earned his PhD in Aerospace Engineering & Mechanics from the University of Minnesota in 2023 under Professor Krishnan Mahesh. Prior to KTH, he served as a Senior Scientist at the Research Center Pharmaceutical Engineering in Graz, Austria (2023-2025), where he led simulation strategy for an EU Horizon 2020 project and developed computational methods for droplet breakup analysis. Nicholas Morse's research centers on: Curvature and rotational effects on turbulent flows Turbulent boundary layers on curved surfaces and spinning cones Eccentric Taylor-Couette-Poiseuille flow transition High-fidelity simulation of complex turbulent flows using DNS and LES His technical expertise spans: High-performance computing infrastructure Direct numerical and large-eddy simulation methodologies Adaptive mesh refinement algorithms Heterogeneous (GPU) computing implementations Multiphase flow modeling Academic recognition includes: John A. & Jane Dunning Copper Fellowship for Aerospace Engineering & Mechanics (2019) Donald & Shirley Gorence Scholarship (2018) Robert H. & Marjorie F. Jewitt Fund Scholarship (2017) Dr. Morse has extensive experience in computational fluid dynamics, having conducted large-scale simulations (>10,000 processors) at the University of Minnesota and developed the Multi-Element Wing Generator MATLAB application for Formula SAE aerodynamics design. His work bridges theoretical fluid mechanics with practical engineering applications across aerospace and pharmaceutical domains.
Rajeev Ahuja is a Professor at the Department of Physics and Astronomy at Uppsala University , specializing in Materials Theory . His research spans computational materials science with a focus on superconductivity , hydrogen storage , and 2D materials for energy and electronic applications. Current affiliations: Uppsala University Key research areas: Condensed matter physics, thermoelectrics, battery materials, and photocatalysis His recent work explores topological states in 2D materials , sensor design using boron rings , and adsorption behaviors of MXenes for biomedical and environmental applications. Collaborative studies with international teams have produced high-impact publications on superconducting hydrides and strain-engineered nanomaterials . Emerging trends in his publications reveal increasing emphasis on twisted heterostructures , transition metal oxides , and environmental remediation via boron nitride and graphene -based systems. Research also extends to planetary material physics (Jupiter/Saturn interiors) and biocompatible nanomaterials .
Dr. Carlos Moyses Graca Araujo is a researcher at Uppsala University's Department of Physics and Astronomy , specializing in Materials Theory . With expertise in condensed matter physics , his work focuses on renewable energy science , particularly battery materials and photo-electro-catalysis . Education : PhD in Condensed Matter Physics (Uppsala University), Postdoc (KTH Royal Institute of Technology, Yale University) His research employs density functional theory , molecular dynamics , and Monte Carlo simulations to investigate energy conversion and storage mechanisms. He has received prestigious awards including the Benzelius Prize , Ångström Premium , and Bjurzon’s Premium for his contributions to materials science. Dr. Araujo's recent publications highlight advancements in aqueous zinc-ion batteries , Li-metal anodes , and polymer solar cells , with a focus on computational materials design and electrocatalyst optimization . His work spans collaborations with institutions like Stanford and Lawrence Berkeley National Laboratory .
Daniel Buncic is Professor of Finance at Stockholm Business School, Stockholm University, specializing in empirical finance, macroeconomics, and econometrics. He holds a Ph.D. in Economics from the University of New South Wales and has held positions at Sveriges Riksbank and the University of St. Gallen. His research integrates machine learning with traditional econometric methods for financial forecasting and policy analysis. Research Focus: Buncic's work spans asset pricing, volatility forecasting, exchange rate dynamics, and macroprudential policy. He employs advanced techniques like Bayesian econometrics, nonlinear time-series models, and high-dimensional data analysis to address questions in financial stability and market predictability. His recent work critiques methodological approaches in natural rate estimation and equity return prediction. Publication Trends: His articles frequently address econometric methodology, financial market volatility, and monetary policy transmission, with a growing emphasis on machine learning applications in finance. Common themes include forecasting under structural breaks, model robustness, and cross-market interdependencies. Grants & Advising: He secured a 1.74M SEK grant (2020-2023) from the Jan Wallander & Tom Hedelius Foundation. Currently advising PhD student Qinglin Ouyang, he previously directed Stockholm University's Master’s in Banking and Finance program (2019-2022).
Erik Lindahl is a Professor of Theoretical Chemistry at the Department of Biochemistry and Biophysics, Stockholm University. His research group is primarily located at the Science for Life Laboratory, a joint research environment for Stockholm University, KTH Royal Institute of Technology, and Karolinska Institutet. Lindahl serves as deputy director of the Swedish e-Science Research Center (SeRC), sits on the steering committee for the national program in Data-driven Life Sciences at SciLifeLab, and is program manager for Stockholm University's part of a joint master's program in molecular techniques for the life sciences. He also serves as vice section dean for chemistry and is involved in creating a Swedish node within CECAM. Lindahl's research focuses on understanding the structure and function of ligand-gated ion channels, particularly in the human nervous system. His group combines experimental and computational approaches, including bioinformatics for building receptor models, molecular dynamics simulations to understand molecular interactions, and experimental techniques like electrophysiology and spectroscopy. They have made significant contributions to understanding how voltage-gated and ligand-gated ion channels function, including determining molecular mechanisms of channel opening and identifying binding sites for molecules that modulate neural signaling. The group is also a leader in developing computational tools for life sciences, particularly the widely used GROMACS software package for molecular dynamics simulations and methods for cryo-electron microscopy data analysis through the RELION program. Analysis of Lindahl's recent publications reveals a strong focus on structural biology of membrane proteins, particularly ligand-gated ion channels. His work integrates cutting-edge computational methods like molecular dynamics simulations, AlphaFold predictions, and cryo-EM data analysis to understand protein conformational dynamics and ligand binding. Key research themes include the structural basis of ion channel function, lipid-protein interactions that stabilize membrane proteins, and computational methods for biomolecular simulation and structural biology. His publications demonstrate a consistent interdisciplinary approach that bridges computational chemistry, structural biology, and neuroscience. Lindahl leads an active research group with multiple postdocs, researchers, and PhD students working on various aspects of membrane protein structure and function. His research is supported by diverse funding sources including the Swedish Research Council, European Research Council, Knut and Alice Wallenberg Foundation, and several EU programs. He plays significant leadership roles in major computational infrastructure initiatives including BioExcel (an EU-funded center of excellence for computational biomolecular research), PRACE (the European computing infrastructure), and EuroHPC. The Lindahl research group operates primarily at the Science for Life Laboratory, where they have access to advanced computational resources and experimental facilities for structural biology. The group collaborates extensively with researchers across Stockholm University, KTH, Karolinska Institutet, and international partners through EU-funded projects. They are particularly active in developing and maintaining open-source software tools that are widely used in the computational biology community.
Johan Henriksson is Associate Professor at Umeå University, Sweden, where he leads the Johan Henriksson Group within the Department of Molecular Biology and the Laboratory for Molecular Infection Medicine Sweden (MIMS), a node of the Nordic EMBL Partnership for Molecular Medicine. He is also affiliated with UCMR (Umeå Centre for Microbial Research) and IceLab (Integrated Science Lab), emphasising cross-disciplinary collaboration. Research in a nutshell. Henriksson’s team explores how T cells function in health and disease, with a special emphasis on cancer and CAR T-cell immunotherapy. By integrating single-cell multi-omics, pooled CRISPR screens, synthetic biology, and machine learning, the group maps gene-regulatory networks that control T-cell identity and anti-tumour activity. They develop wet-lab protocols (e.g., molecular inversion probes for unbiased quantification) and open-source computational tools—including the Nando framework for gene-expression prediction and high-performance pipelines written in Rust—to extract robust biological knowledge from very large datasets. Major research directions. Discovery of a novel JUNB + CD4 T-cell state linked to memory formation, circadian rhythm, and CAR T-cell exhaustion. Single-cell telomere measurements for early cancer detection, particularly pancreatic cancer. Pooled CRISPR screening in primary human T cells and in Plasmodium falciparum to uncover essential regulators. Extension of single-cell technologies to microbial systems in collaboration with Laura Carroll, Kemal Avican, and Linas Mažutis. Development of the Bascet/Zorn pipeline for reference-free comparative genomics across mixed microbial communities. Advocacy and engineering of the Rust programming language for safe, high-performance bioinformatics. Collaborative network. Henriksson actively collaborates with experts who contribute complementary biological insight: Isabelle Magalhães (CAR T-cell therapy), Nicole Boucheron (T cells in allergic asthma), Mattias Forsell (B-cell responses), Anna Överby (tick-borne encephalitis), Annasara Lenman (SARS-CoV-2), Tommy Löfstedt (machine-learning latent-space models), Ellen Bushell ( Plasmodium gene essentiality), and many others. Training & mentorship. The group currently hosts PhD students (e.g., Ionut Sebastian Mihai) and welcomes additional students and postdocs interested in combining wet-lab immunology with computational innovation, especially those eager to adopt Rust for large-scale data analysis.
Professor Evgeny Osipov is a full professor in Dependable Communication and Computation Systems at Luleå University of Technology, Department of Computer Science within the Department of Systems and Space Engineering. His research focuses on Communication and computing systems, with particular expertise in Artificial Intelligence frameworks. His educational background includes: PhD in Computer Science (Cum Laude) from University of Basel, Switzerland (2005) Licentiate of Technology in Telecommunications from KTH Royal Institute of Technology, Sweden (2003) Pre-doctoral school in Communication Systems from EPFL, Switzerland (1999) Engineer degree with Honors from Krasnoyarsk State Technical University, Russia (1998) Professor Osipov's research interests center around Vector Symbolic Architectures (also known as hyperdimensional computing), which serves as a bridge between symbolic and connectionist AI approaches. His work explores how mathematical properties of random hyperdimensional spaces can be leveraged for AI functionality, with potential applications in creating artificial general intelligence. His research is particularly relevant for low-resource machine learning tasks, such as those encountered in wearable Internet of Things devices. His recent publications (2024-2025) demonstrate a strong focus on improving classification performance using hyperdimensional computing techniques. He has explored confidence-driven training of centroids, implementations for spiking neural networks, and margin-based training approaches across numerous datasets to validate these techniques. Professor Osipov has received research funding from several notable organizations: Swedish Foundation for Strategic Research (grants UKR22-0024, UKR24-0014) Swedish Research Council (grants GU 2022/1963, 2022-04657) Luleå University of Technology Flemish Government Scholars at Risk (SAR) His active publication record across multiple high-impact journals indicates ongoing research activity and collaboration. His work on Vector Symbolic Architectures represents a significant contribution to the field of efficient AI computation, particularly for resource-constrained environments where traditional deep learning approaches would be impractical.
