جستجوی استاد راهنمای دانشگاهی

Prof. Joost Reek is a Professor at the University of Amsterdam's Faculty of Science and leads the Homogeneous, Supramolecular and Bio-Inspired Catalysis group within the Van 't Hoff Institute for Molecular Sciences. He serves as scientific director of NIOK (Netherlands Institute for Catalysis Research), a nationwide graduate school coordinating catalysis research across Dutch universities under recognition by the Royal Netherlands Academy of Sciences (KNAW). His research focuses on supramolecular chemistry and catalysis, with expertise in supramolecular cages, homogeneous catalysis, sustainable chemistry, solar fuels, and electrochemical conversions. The group develops novel catalysts for industrial reactions (hydroformylation, hydrogenation), biomass conversion, and solar fuel generation including water oxidation and CO 2 reduction. Recent work explores nanocage systems for bioorthogonal chemistry and photoactive catalytic nanospheres. Reek's team secured a €10.6 million Synergy grant in 2023 for brain cancer research involving nanocages delivering small interfering RNAs. They maintain industry partnerships through NIOK's VIRAN advisory board and operate a spin-off company translating molecular research into applications. The group is embedded within the Van 't Hoff Institute for Molecular Sciences, utilizing advanced facilities for molecular design and catalytic testing. Their interdisciplinary approach bridges fundamental supramolecular chemistry with sustainable energy solutions and biomedical innovations.

• Supramolecular Chemistry
• Homogeneous Catalysis
• Sustainable Chemistry
• +۵ مورد دیگر

Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.

• Biomedical Signal Processing
• Diagnostic Imaging
• Ultrasound Technology
• +۴ مورد دیگر

Dr. Peter J.F. Lucas is a Full Professor specializing in Datamanagement & Biometrics with over 35 years of experience in artificial intelligence, probabilistic graphical models, and clinical decision support systems. His research spans intelligent systems, machine learning, and eHealth, with a focus on applying Bayesian networks and probabilistic logic to medical and non-medical domains.

• Artificial Intelligence
• Bayesian Networks
• Machine Learning
• +۶ مورد دیگر

Qian Tao is an Assistant Professor at the Department of Imaging Physics , Faculty of Applied Sciences , Delft University of Technology . She previously worked at the Division of Image Processing, Department of Radiology, Leiden University Medical Center from 2009 to 2020. Academic Background: BSc in Electrical Engineering (Fudan University), MSc in Biomedical Engineering (Fudan University), PhD in Biometric Authentication (University of Twente) Research Interests: Focus on trustworthy AI methodologies for critical healthcare applications, including medical imaging for patient diagnosis and clinical intervention. Specializes in cardiac MRI analysis, image-guided interventions for cardiac arrhythmias, and AI in Radiology. Publication Trends: Recent work emphasizes motion correction in cardiac MRI, deep learning for image registration, and novel techniques like TRAFF2 mapping. Keywords include Medical Imaging , Machine Learning , Cardiac MRI , and Quantitative Analysis . Contact: Email: Q.Tao@tudelft.nl

• Artificial Intelligence
• Medical Imaging
• Cardiac MRI Analysis
• +۳ مورد دیگر

Pim de Vink is a doctoral researcher at Eindhoven University of Technology , affiliated with the Biomedical Engineering department and Chemical Biology group. Supervised by dr. L.-G. Milroy and prof. L. Brunsveld , his work bridges supramolecular chemistry and chemical biology , focusing on host/guest chemistry for protein complex modulation. Education: B.Sc. in Chemistry (2014) from University of Amsterdam M.Sc. in Biomedical Engineering (2016) from TU/e Internship at Max Planck Institute for Molecular Physiology (2016) on gold-catalyzed synthesis Research Themes: His research develops switchable cucurbituril-based systems for light-controlled enzyme activation and artificial signaling networks. Key areas include protein-protein interaction stabilization , thermodynamic modeling , and allosteric nuclear receptor modulation . Publication Trends: Across JACS , Chemical Science , and RSC Chemical Biology , his work from 2017–2023 emphasizes supramolecular tools for biochemical applications. Notable contributions include 100-fold affinity enhancement of 14-3-3 ligands and UV-responsive cucurbituril release mechanisms . Grants: Funded by Netherlands Organization for Scientific Research (NWO) through Gravity program 024.001.035 and VICI grant 016.150.366.

• Supramolecular Chemistry
• Chemical Biology
• Protein-Protein Interactions
• +۳ مورد دیگر

Matthias Barz is a Professor of Biotherapeutic Delivery at the Leiden Academic Centre for Drug Research (LACDR) , Faculty of Science , Leiden University . He leads the Barz Lab , focusing on polymer science and biomedical applications of functional nanoparticles. Professor of Biotherapeutic Delivery (Leiden University) Head of the Division of BioTherapeutics Researcher in reactive polymer systems and nanocarrier design His research bridges polymer chemistry with biomedical applications, emphasizing polypept(o)ide-based nanocarriers for targeted drug delivery in cancer, inflammation, and neurodegenerative diseases. Key areas include: Stimuli-responsive polymer architectures Secondary structure-driven self-assembly Core-crosslinked micelles for controlled cargo release Redox-sensitive disulfide bonds for intracellular delivery Protein-repellent nanoparticle shells Riboflavin-functionalized nanocarriers for tumor targeting The lab's publications highlight advancements in polysarcosine-containing copolymers , orthogonal functional group utilization , and modular nanoparticle platforms with precise control over morphology and function. Current projects explore the clinical translation of these systems for immunotherapy and diagnostics. Scientific recognition includes: Dozentenpreis des Fonds der Chemischen Industrie (2018) PMSE Young Investigator Award (2018) Nachwuchswissenschaftlerstipendium der GDCh (2017) His team trains graduate students in polymer synthesis, nanoparticle characterization, and biomedical application testing. Collaborations span institutions like the University of Tokyo and Johannes Gutenberg University Mainz , with ongoing projects under the SFB 1066 initiative for malignant melanoma immunotherapy.

• Polymer Science
• Biotherapeutic Delivery
• Nanoparticle Synthesis
• +۷ مورد دیگر

Michel Versluis is a Full Professor at the University of Twente, Netherlands, specializing in Physical and Medical Acoustics within the Physics of Fluids group. His work focuses on microbubbles and microdroplets for medical imaging and therapy, as well as microfluidic applications in medicine and nanotechnology. University of Twente, Physics of Fluids group His research bridges physics and biomedical engineering, with publications in high-impact journals like PNAS and IEEE Transactions. Recent work emphasizes ultrasound-driven microbubble dynamics, additive manufacturing of flow phantoms, and deep learning for super-resolution imaging. 2025 publications: vascular phantoms, PROTEUS simulator, acoustic microbubble control 2024 innovations: 3D-printed medical devices, immunogenic cell death optimization Contact: m.versluis@utwente.nl

• Physical and Medical Acoustics
• Microbubbles
• Microdroplets
• +۵ مورد دیگر

Mathieu Odijk is a Full Professor at the University of Twente's Faculty of Science and Technology, leading the Integrated Devices and Systems department. His research focuses on microfluidic systems, catalysis, and organ-on-chip platforms, with contributions to UN Sustainable Development Goals through advanced material characterization and biomedical engineering. He has authored over 120 publications and holds an h-index of 27 with 1,820 citations. Expertise: Microfluidics, catalyst particle diagnostics, SERS substrates, organ-on-chip systems, and spectroscopic techniques. Collaborations include Weckhuysen (catalysis), van den Berg (microfluidics), and Meirer (materials science). Key projects: Modular organ-on-chip platforms (STARTER), droplet-based catalyst screening, and real-time reaction monitoring via ATR-IR systems. His research combines nanotechnology and chemical engineering to develop tools for sustainable energy, environmental remediation, and biomedical applications. Recent work includes microreactors for catalyst particle analysis, light-driven urea oxidation for wearable kidney devices, and standardized platforms for organ-on-chip research.

• Microfluidics
• Catalyst Particle Research
• Organ-on-Chip Platforms
• +۳ مورد دیگر

Hyosang Lee is an Assistant Professor in the Robotics Section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He holds a PhD from KAIST and has held research positions at the Max Planck Institute and University of Stuttgart. His work focuses on tactile sensing technologies, including artificial skin development, soft robotics, and integration of sensory systems with AI. Bachelor's: Mechanical Engineering, Korea University Master's: Robotics and Mechanical Engineering (double major) PhD: Mechanical Engineering, KAIST (2017) Research interests span tactile sensor design, electrical impedance tomography (EIT), and human-robot interaction. His group emphasizes creating scalable, flexible tactile systems for robots. Recent work includes air pressure sensing for force estimation and biomimetic skin materials. Publications highlight innovations in multi-directional force sensing, soft component technologies, and haptic interfaces for autism therapy. He teaches 'Dynamics and Control of Robotic Systems' and serves on the editorial board of npj Robotics . No formal student advisees are listed, though his lab, the Tactile Sensing and Robotic Skin Group , likely involves graduate researchers. His research contributes to UN Sustainable Development Goals related to health and technology.

• Tactile Sensing
• Robotics
• Soft Robotics
• +۴ مورد دیگر

Dr. Sonja Pullen is a Visiting Professor at the University of Amsterdam's Faculty of Science, affiliated with the Van 't Hoff Institute for Molecular Sciences. Her research focuses on photocatalysis, coordination chemistry, and supramolecular systems, with particular emphasis on developing sustainable energy conversion technologies. Key areas include molecular catalyst design, confined-space catalysis, and light-driven chemical transformations. Her work integrates advanced spectroscopic techniques (e.g., ultrafast spectroscopy) to study catalytic mechanisms, particularly in systems like diiron complexes and metal-organic frameworks (MOFs). Recent projects explore oxygen-tolerant catalysts, substrate-binding effects in photocatalytic dehalogenation, and the role of hydrogen bonding in catalytic activity. She also investigates functional materials such as coordination cages for artificial photosynthesis. Dr. Pullen’s publications highlight breakthroughs in catalyst stability, reaction selectivity, and energy-efficient processes. Her interdisciplinary approach bridges organic/inorganic chemistry, materials science, and renewable energy applications. Current trends in her work emphasize environmental sustainability and scalable photocatalytic systems for hydrogen production and CO2 conversion. Her lab at the Van 't Hoff Institute collaborates widely on topics like molecular encapsulation, MOF functionalization, and bioinspired catalysts. Ongoing projects aim to enhance photocatalytic efficiency through structural design and confinement strategies.

• Photocatalysis
• Coordination Chemistry
• Renewable Energy
• +۳ مورد دیگر

Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.

• Artificial Intelligence
• Machine Learning
• Drug Discovery
• +۸ مورد دیگر

Prof. Wouter Roos is a Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Molecular Biophysics department at the Zernike Institute for Advanced Materials. His research focuses on viral dynamics, membrane assemblies, and protein mechanics, utilizing advanced techniques like High Speed Atomic Force Microscopy (HS-AFM) and optical tweezers. Education: Studied Physics at the Universiteit van Amsterdam, earned a PhD from the Universität Heidelberg under Joachim Spatz. Conducted postdoctoral research at Max-Planck-Institut, Institut Curie, and Vrije Universiteit before joining Groningen in 2015. Research Interests: Physical Virology (viral material properties and dynamics), membrane biophysics (synthetic cells and lipid interactions), and molecular motor systems. His work bridges physics, chemistry, and biology to understand nanoscale biological processes. Recent Article Trends: Studies on hybrid membranes for synthetic cells, leukemic cell mechanics, and antibiotic-membrane interactions highlight his interdisciplinary approach. Key techniques include HS-AFM and single-particle tracking. Awards: Received a VIDI grant and multiple national/international grants. His lab leads the oLife Co-Fund consortium and participates in the MOSBRI research infrastructure. Grants & Leadership: Coordinates the oLife Fellowship Programme and chairs the Molecular Biophysics Lab. Active in steering committees for EU-funded initiatives. Labs/Teams: Heads the Molecular Biophysics Lab, focusing on viral dynamics and membrane systems. Collaborates globally on projects like ESCRT-III polymerization and antibiotic mechanisms.

• Biophysics
• Biochemistry & Molecular Biology
• Nanoscience & Nanotechnology
• +۵ مورد دیگر

Nezihe Merve Gürel is an Assistant Professor in Computer Science at Delft University of Technology (TU Delft), affiliated with the Pattern Recognition & Bioinformatics Group within the Intelligent Systems Department of the Faculty of Electrical Engineering, Mathematics and Computer Science. Her research focuses on developing robust, reliable, and efficient machine learning methods with enhanced reasoning capabilities, bridging theoretical rigor and practical applications. She emphasizes data-centric approaches to improve ML systems. Education: PhD in Computer Science from ETH Zurich, MSc from EPFL (Switzerland). Research Interests: ML robustness, reliability, reasoning, data-centric ML, federated learning, and explainable AI. Her recent work includes certified robustness for retrieval-augmented models and time-efficient learning algorithms. She has contributed to the Journal of Data-centric Machine Learning Research as an executive editor and served as a reviewer for top ML conferences (NeurIPS, ICML, ICLR). She previously held roles at IBM Research, Stanford University's Human-Centered AI Lab, and Westlake Institute for Advanced Study. Her awards include the Generation Google Scholarship and Cisco Research Funding . Scientific Awards : Generation Google Scholarship (2021) Cisco Research Center University Funding Labs & Teams : She leads research in the Pattern Recognition Laboratory at TU Delft and collaborates with international institutions like Stanford and Westlake Institute for Advanced Study.

• Machine Learning
• Computer Science
• Pattern Recognition
• +۴ مورد دیگر

Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.

• Neuromorphic Computing
• Memristor-Based Architectures
• Energy-Efficient AI Hardware
• +۷ مورد دیگر

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +۴ مورد دیگر