جست‌وجوی استاد و پژوهشگر

• Powder Metallurgy
• Sinter Technology
• Additive Manufacturing
• +۶ مورد دیگر

Kim Vanmeensel is a Senior Lecturer at the Faculty of Engineering Sciences, Department of Materials Science at KU Leuven. He is affiliated with the Functional Materials research group (SIEM) and the Sustainable Materials Management unit (SeMPeR) at the Arenberg campus. Additionally, he is a member of the Materials Science Division and Leuven.AM – KU Leuven Institute for Additive Manufacturing. His research focuses on powder metallurgy, sinter technology, additive manufacturing (particularly Laser Powder Bed Fusion), metallic alloys and metal matrix composites, and thermal-electromagnetic-mechanical finite element simulations. His work spans both fundamental materials science and applied engineering solutions for advanced manufacturing processes. His most recent publications demonstrate a strong trend toward optimizing additive manufacturing processes, particularly Laser Powder Bed Fusion, for various metal alloys and composites. His research examines microstructure-property relationships, thermal treatments, and computational modeling to enhance material performance in applications ranging from structural components to specialized engineering materials. Kim Vanmeensel serves as promotor or co-promotor for numerous research projects including Near-net Shape Shaping of Complex Components, Multi-SCOPE (Multi-scale imaging platform), and development of recycled Nd-Fe-B permanent magnets. He has secured substantial research funding for projects running through 2028. He actively supervises students, with recent theses focusing on micro-mechanical characterization of heat-treated Ti-6Al-4V and fatigue of aluminum matrix composites processed by Laser Powder Bed Fusion. His laboratory work involves advanced characterization techniques and computational modeling for additive manufacturing processes.

• Photonic Crystals
• Optical Modeling
• Materials Science
• +۳ مورد دیگر

Cedric Vandenbem is a Researcher at the University of Namur , with a focus on photonic crystals, optical modeling, and materials science. His work spans interdisciplinary domains including nanotechnology, biomaterials, and computational physics. Education: PhD in Sciences (2006), Master in Physics (2003), DEA in Physics and Material Chemistry (2005), Master in Management (2013). Research interests center on photonic bandgap materials, light-matter interactions, and structural color phenomena in biological systems. Key projects include modeling photonic polycrystals in beetles and studying enhanced backscattering in nanocomposites. His recent publications (2007-2013) highlight collaborations in optical simulations and experimental validation. Activities include participation in conferences like the International Society for Optical Engineering and workshops on nanosciences. No scientific awards are documented in the scraped text.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Jan Lemeire is an active researcher at Vrije Universiteit Brussel (VUB), affiliated with the Department of Electronics and Informatics within the Faculty of Engineering. Based in Brussels, Belgium at Pleinlaan 2, he maintains an active research profile with an h-index of 10 and 581 citations according to Scopus data. His research interests span multiple domains including GPU computing, machine learning, embedded systems, and their applications in diverse fields from biomedical engineering to forensic science. His work demonstrates strong interdisciplinary connections, bridging computer science with practical applications in health technology, crime analysis, and industrial systems. Analysis of his recent publications reveals a consistent focus on computational efficiency, with particular emphasis on GPU acceleration, embedded AI deployment, and machine learning applications. His work shows evolution from hardware-focused research toward more applied domains including healthcare technology and crime pattern analysis. Dr. Lemeire actively participates in multiple research projects including NSIS2: PRISMA network (2024-2029), IOF3016: GEAR (2021-2025), and Tech4Health (2024-2025), demonstrating sustained research funding and collaborative work across disciplines. His academic activities include supervision of graduate students, as evidenced by his role as advisor for the 2017 Master's thesis on GPU-accelerated holography, and regular participation in major conferences including the Conference on Uncertainty in Artificial Intelligence and the International Symposium on Embedded Multicore Systems. Dr. Lemeire maintains an active research laboratory focused on computational methods, with particular strengths in parallel processing techniques and their application to real-world problems across multiple domains including healthcare, industrial systems, and forensic science.

David Blinderمشاهده پروفایل

استاد

• Optics
• Signal Processing
• Computer Science
• +۷ مورد دیگر

David Blinder is a Professor at the Vrije Universiteit Brussel (VUB) in the Department of Electronics and Informatics (ETRO), where he maintains a dual affiliation with the imec research institute. He concurrently holds a JSPS International Research Fellow position at Chiba University, leveraging over a decade of cross-institutional expertise in optics, signal processing, and computer science to advance computational holography and photonics. His research program centers on signal processing in photonics and holography, with emphasis on numerical diffraction, time-frequency analysis, data compression, and computational imaging. As lead editor of the JPEG Pleno Holography standard, he drives innovation in holographic data representation and transmission. His work integrates optical setups, holographic displays, and high-performance computing to solve complex problems in 3D visualization and inverse methods. Recent publications demonstrate a cohesive trajectory toward efficient computational frameworks for holographic systems, with recurring themes in numerical diffraction algorithms, polygon-based hologram generation, and asymmetric point-spread function optimization. These advances directly enable next-generation holographic displays and computational imaging applications across medical visualization and augmented reality. Blinder's contributions have been recognized through significant honors: Editor's Pick award (October 2024) Editor's Pick award (September 2024) Suzuki-Okada Memorial prize (June 2024) OSA Outstanding Reviewer recognition (2018) He has mentored master's students including A. Ouahabi (2015, Motion Estimation for Dynamic Holography) and F. Suarez Groen (2017, GPU-optimized Computer-Generated Holography). His research is sustained by major grants: FWOAL1100: Advanced dynamic computer-generated holography (2024-2026) FWOAL1101: Computational Incoherent holographic plenoptic camera (2024-2027) FWOTM1099: Chirplet-based diffraction framework (2022-2027) Tech4Health: Future health technologies (2024-2025) As a core member of VUB's ETRO department and imec, Blinder leads interdisciplinary teams in projects like GEAR and Tech4Health, developing holographic visualization systems for medical applications while advancing fundamental optical computing methodologies.

• Flow Chemistry
• Hydrodynamics
• Liquid Chromatography
• +۵ مورد دیگر

Sander Deridder is a researcher specializing in Chemical Engineering and Separations Science , with a focus on applying computational fluid dynamics to chromatographic systems. His work bridges analytical chemistry, fluid mechanics, and microscale engineering, particularly in optimizing separation techniques under extreme conditions (e.g., pressures up to 2500 bar). Research Interests Flow Chemistry Hydrodynamics Liquid Chromatography Thermal Conductivity Diffusion Recent Publications 2023: Studies on supercritical fluid chromatography (SFC) and capillary misconnection effects. 2022: Thermal band broadening analysis in ultra-high-pressure liquid chromatography. Conference Activities Speaker at the 2023 International Symposium on Chromatography (ISC 2023). Contributor to multiple 2024 and 2025 conference presentations on thermal control in chromatography.

• Liquid Chromatography
• Separation Science
• Chemical Engineering
• +۳ مورد دیگر

Gert Desmet is a Full Professor at Vrije Universiteit Brussel , leading research in the Department of Chemical Engineering and Separations Science within the Chemical Engineering and Applied Biological Sciences faculty. With an h-index of 49 and over 842 research outputs, his work focuses on advanced liquid chromatography systems and separation science innovations. 1995–2029: Active fundamental and applied research projects 2020–2025: Supervised 102 research works and 3 PhD theses His research explores stationary phase design , dispersion modeling , and 3D-printed chromatography media , with recent emphasis on AI-driven peak detection and theoretical plate height modeling. He leads international collaborations like the VUB-Universiteit Twente joint PhD program. Notable scientific awards include: First Prize - HPLC 2008 & 2020 Poster Awards First Prize - Analytical Science Advances Best Poster Second Prize - Udias Master Thesis Award 2012 As principal advisor, he has guided doctoral research on topics such as: Gradient Stationary Phase Selectivity (2015) 3D-Printed Sample Preparation (2025) Computational Flow in Porous Media (2019)

• Materials and Structures for Sustainable Construction
• Hybrid Structures (Concrete/Timber/Steel)
• Multiscale-Multiphysics Modeling
• +۵ مورد دیگر

Lin Wan-Wendner is an Assistant Professor at the Department of Civil Engineering, KU Leuven, with a focus on materials and structures for sustainable construction. They are affiliated with the Faculty of Engineering Technology and lead research on hybrid structures combining concrete, timber, steel, and novel materials, as well as multiscale-multiphysics modeling of structural behavior and 3D concrete printing. Research Highlights : Sustainable structural design, hybrid material systems, computational modeling, and additive manufacturing in construction. Current Projects : Promotor for initiatives like HYBRE (demountable hybrid structures), 3D4WALL (3D-printed wall elements), and climate-resistant composite materials. Engagement : Committee member for steel-timber composites, editorial board member of Low-carbon Materials and Green Construction , and reviewer for journals including Engineering Structures and Structural Concrete . Recent publications emphasize chloride binding in geopolymers, recycled aggregate modeling, and machine learning applications for concrete durability. Their work bridges theoretical and applied research in structural engineering.

• Nuclear Physics
• Particle Physics
• High-Energy Physics
• +۳ مورد دیگر

Juan Cabrera Jamoulle is affiliated with the Faculty of Sciences at the University of Namur. He holds roles within the Namur Institute for Complex Systems (naXys), the Technological Platform of High Performance Computing, and the Namur Institute of Structured Matter (NISM). His research focuses on nuclear processes, including fusion-fission and fusion-evaporation mechanisms in high-energy collisions such as 20 Ne + 159 Tb at 8–16 MeV/nucleon. He completed his doctoral studies at the Université Catholique de Louvain, earning a Doctor of Science in 2002. Education: Doctor of Science, 2002 (Université Catholique de Louvain). His research interests span nuclear physics, particle interactions, and computational modeling, with affiliations emphasizing interdisciplinary approaches to complex systems and material sciences. Collaborations likely involve high-performance computing for simulating nuclear reaction dynamics. No scientific awards or grant details are explicitly mentioned in the provided text. He has not listed advisees or publications here, though his academic contributions are implied through his institutional roles.

Yvan Saeysمشاهده پروفایل

استاد

• Machine Learning
• Interpretable AI
• Large-Scale Data Mining
• +۳ مورد دیگر

Yvan Saeys is a Professor of Machine Learning at Ghent University and a Principal Investigator in Systems Immunology at VIB (Flanders Institute for Biotechnology). He obtained his PhD in Computer Science from Ghent University and VIB, with postdoctoral research at the University of the Basque Country and Université Claude Bernard. His work bridges machine learning and biomedical data analysis, particularly in single-cell transcriptomics and cytometry. Develops advanced data mining and machine learning techniques for biological and medical applications Focuses on regulatory network inference and biomarker discovery from high-throughput datasets Leads the DAMBI research group at the Inflammation Research Centre His methods have achieved top performance in international challenges like DREAM5 and FlowCAP-IV. With over 100 publications and 9,500 citations, he specializes in computational models for single-cell data analysis, interpretable AI, and large-scale data mining. Recent projects include tools like CytoNormPy , SPArrOW , and Spotless for spatial and single-cell omics workflows. Scientific Awards Best performing team at the DREAM5 challenge Best performing team at the FlowCAP-IV challenge Research Trends Recent 2024-2025 publications emphasize single-cell analysis , spatial omics , immune profiling , and machine learning explainability . Key subfields include cytometry normalization , cell-cell communication , biomarker discovery , network inference , adversarial robustness , and multiomics integration .

Sofie Van Hoeckeمشاهده پروفایل

دانشیار

• Machine Learning
• Semantic Technologies
• Predictive Maintenance
• +۷ مورد دیگر

Sofie Van Hoecke is an Associate Professor at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab) - imec. She leads the PreDiCT (Predictive Diagnostics through Contextual and Trustworthy AI) team, which develops innovative machine learning solutions for predictive maintenance and healthcare diagnostics. Her educational background includes: Engineering Degree, Ghent University (2003) PhD in Computer Science Engineering, Department of Information Technology, Ghent University (thesis: 'Efficient service management in healthcare') Van Hoecke's research centers on hybrid AI systems that fuse machine learning with semantic technologies and physical modeling. Her work targets predictive maintenance (e.g., condition monitoring via thermal imaging and vibration analysis) and predictive healthcare (e.g., migraine tracking, COPD management, and infection diagnostics). Key specialties include context-aware modeling, expert-driven machine learning, and dynamic semantic dashboards for real-time decision support. She has pioneered applications in industrial IoT, medical diagnostics, and environmental monitoring. Her 2025 publications reveal a strong interdisciplinary trajectory applying knowledge graphs, conformal prediction, and explainable AI to diverse domains including network security, digital pathology, building inspection, and neurology. A unifying theme is trustworthy AI with emphasis on uncertainty quantification, out-of-distribution robustness, and seamless integration of domain knowledge into ML pipelines. Her work bridges theoretical advances with industrial deployment through imec collaborations. Scientific Awards: No scientific awards mentioned in provided sources Van Hoecke directs the PreDiCT team across multiple EU and industry-funded projects including AI-SWEEP-2 (smart wound care), HEROI2C (ICU infection management), mBrain (migraine monitoring), and SmartWaterConnect 5.0 (water grid analytics). While specific student advisees aren't listed, her leadership of a 15+ member research team and graduate teaching duties indicate active mentorship. Current grants focus on deploying context-aware AI in healthcare (S.E.P.S.I.S. Connect, ADAM) and industrial settings (RE-ENNOVATE, PACSOI). The PreDiCT team operates within IDLab's Ghent University-imec ecosystem, maintaining strong industry partnerships with Siemens, UZ Ghent Hospital, and water management authorities. Their research infrastructure includes specialized testbeds for predictive maintenance validation and clinical trial frameworks for digital health applications. Ongoing work emphasizes real-world validation of hybrid AI models in resource-constrained environments, particularly for neglected tropical diseases and chronic condition management.

• Applied Mathematics
• Computational Science
• Plasma Physics
• +۷ مورد دیگر

Giovanni Samaey is a Professor of Applied Mathematics and Mathematical Engineering at KU Leuven's Faculty of Engineering Science. He leads research in computational and multiscale methods, focusing on plasma edge modeling for nuclear fusion reactors, Bayesian inversion, and Monte Carlo algorithms. Appointed in 2011, he currently supervises ten PhD students and has held a five-year membership in the Young Academy. His work bridges academic research with societal impact, co-founding Platform Wiskunde Vlaanderen to strengthen mathematics in Flanders. Education: Graduated in Computer Science (specializing in applied mathematics) from KU Leuven (1996), completed a PhD in 2001 under Prof. Dirk Roose, supported by an NFWO fellowship. He transitioned from engineering studies due to a passion for mathematics' societal impact, initially avoiding academia but ultimately embracing teaching and research. Research Interests: Development of numerical methods for multiscale phenomena, including micro-macro acceleration algorithms, multilevel Monte Carlo techniques, and hybrid fluid-kinetic models. His work addresses challenges in plasma physics, fusion energy systems, and inverse problems. Key contributions include the X-Factor book (with Joos Vandewalle) promoting mathematics outreach and advancing computational tools for plasma edge simulations. Awards/Honors: Member of the Young Academy (2016-2021), NFWO Aspirant Fellowship (2001-2002). His efforts in STEM advocacy and mathematics promotion through Platform Wiskunde Vlaanderen highlight his dedication to education and public engagement. Advising & Leadership: Supervises a team of PhD students in applied mathematics and computational science. Active in curriculum development and interdisciplinary collaborations, particularly in fusion energy modeling. His research group contributes to codes like EMC3-EIRENE for plasma edge simulations. Labs/Teams: Leads projects in multiscale numerical methods and plasma simulation within KU Leuven's engineering faculty. Collaborates internationally on fusion reactor modeling and Monte Carlo algorithm design, emphasizing computational efficiency and scalability.

Luc Henrardمشاهده پروفایل

استاد

• Solid State Physics
• Nanomaterials
• Plasmonics
• +۳ مورد دیگر

Luc Henrard is a Full Professor of Physics at the University of Namur, specializing in solid-state physics and nanomaterials. He directs research on 2D materials, plasmonics, and computational modeling of electronic properties. Previously head of the Physics Department (2017-2020), he established the university's High-Performance Computing Center. His group investigates growth mechanisms of graphene/boron nitride heterostructures and plasmonic properties of metal oxides for electrochromic applications. Recent publications focus on CVD optimization for 2D materials and jellium system plasmonics.

Hamid Hamedمشاهده پروفایل

پژوهشگر ارشد

• Electrochemical Engineering
• Battery Technology
• Material Science
• +۳ مورد دیگر

Hamid Hamed is an FWO Postdoctoral Fellow and project leader at Hasselt University's Department of Electrochemical Engineering, focusing on physics-based and data-driven models for advanced batteries. His work integrates non-equilibrium thermodynamics and multicomponent transport theory in porous media. Education: B.Sc. (2013) and M.Sc. (2016) in Chemical Engineering from Sharif University of Technology (Iran). His Bachelor’s thesis addressed in-situ groundwater remediation, while his Master’s explored dialysis membrane microstructure optimization. He completed his Ph.D. (2017–2021) at Hasselt University under Prof. M. Safari, studying mesoscale properties-performance relationships in batteries. Research interests span electrochemical engineering, battery modeling, and sustainable materials for energy storage. His projects address challenges in lithium-ion batteries, sodium-ion systems, and supercapacitors, with a focus on improving energy density and operational efficiency through material design and process optimization. Notable contributions include studies on lithium metal electrode dendrite growth, sodium-ion battery electrolyte strategies, and biomass-derived electrode materials. His work bridges theoretical modeling with experimental validation, emphasizing practical applications in energy storage systems.

Tim Huegeمشاهده پروفایل

استاد

• Cosmic Rays
• Astroparticle Physics
• Radio Detection of Air Showers
• +۴ مورد دیگر

Tim Huege is a Professor of Astrophysics at the Inter-University Institute for High Energies (Vrije Universiteit Brussel) and group leader of the Cosmic Ray Simulations group at the Karlsruhe Institute of Technology . His research focuses on radio detection of cosmic ray air showers, development of simulation frameworks (CoREAS, CORSIKA8), and applications of Information Field Theory in astroparticle physics. He serves as task leader in the Pierre Auger Collaboration and as co-task leader for radio detection activities. Research Interests include: Radio emission from extensive air showers Cosmic ray energy estimation via geosynchrotron Neutrino detection using radio arrays Interferometric reconstruction techniques Atmospheric effects on particle cascade simulations Scientific Leadership involves: Lead developer of CoREAS simulation code Coordinator for CORSIKA8 project Member of SKA High-Energy Cosmic Particles Working Group Former contributor to LOPES, Tunka-Rex, and KASCADE-Grande collaborations His work spans detector calibration, shower parameter reconstruction, and multi-messenger astrophysics with key publications in Astroparticle Physics , Physical Review D/Letters , and JCAP . Current projects address noise compensation in radio detection, inclined shower simulations, and neutrino astronomy.

Guillaume Crevecoeurمشاهده پروفایل

دانشیار

• Dynamical Systems
• Nonlinear Optimal Control
• Model-Based Optimization Algorithms
• +۴ مورد دیگر

Guillaume Crevecoeur (born 1981) serves as Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Electrical Energy, Metals, Mechanical Constructions, and Systems. He leads research at the Electrical Energy Laboratory (EELAB) and is an affiliate member of Flanders Make, the strategic research center for manufacturing industry. His international collaborations include membership in scientific steering committees and prior visiting researcher positions at Technical University Ilmenau and Physikalische Technische Bundesanstalt in Berlin. Crevecoeur earned his MSc (2004) and PhD (2009) in Engineering Physics from Ghent University, followed by a postdoctoral fellowship with Research Foundation Flanders (FWO-Flanders). His research spans dynamical systems modeling, optimization, and control with foundational work in model-based algorithms, inverse problems, and nonlinear optimal control. Current strategic focus areas include machines and motion systems, particularly airborne wind energy applications and robotics. Analysis of his 2022-2025 publications reveals dominant trends in physics-informed machine learning for control systems , with significant contributions to reinforcement learning for underactuated systems, tactile sensing in robotics, and computational fluid dynamics for renewable energy. His work consistently integrates model-based optimization with data-driven approaches, especially in wind turbine control and aerial robotics co-design. Best Paper Award at 22nd International Conference on System Theory, Control and Computing (2018) As Principal Investigator, Crevecoeur directs research funded through FWO-Flanders and industry partnerships with Flanders Make. His lab focuses on mechatronic systems development, particularly in robotic manipulation and renewable energy conversion. Current projects include airborne wind energy system optimization, collision-tolerant robotics, and digital twin implementations for industrial machinery. He actively contributes to international scientific steering committees and serves as academic advisor for graduate students in electrical energy systems. Crevecoeur's research group operates within EELAB's infrastructure, utilizing high-performance computing resources for computational fluid dynamics simulations and experimental facilities for mechatronic system validation. The team collaborates with industry partners through Flanders Make on industrial automation challenges, with recent work focusing on real-time control for high-speed manufacturing processes and adaptive energy management systems.