جست‌وجوی استاد و پژوهشگر

Anubhav Goelمشاهده پروفایل

پژوهشگر

• Renewable Energy Engineering
• Solar Thermal Systems
• Desalination
• +۷ مورد دیگر

Anubhav Goel is a Researcher at the Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University. His work bridges engineering, sustainability, and operations, with a strong focus on renewable energy systems and industrial process optimization. Research Interests: Renewable Energy Engineering, particularly solar thermal systems Desalination technologies and water-energy nexus Artificial Neural Networks for process prediction Optimization algorithms including genetic and swarm methods Industrial decarbonization and energy efficiency Dynamic modeling and control of thermal systems The recent publications (2023–2024) indicate a consistent research trajectory in sustainable energy systems, with a focus on parabolic trough solar collectors, reverse osmosis desalination, and hybrid AI-optimization models. His work often integrates techno-economic analysis and sustainability metrics. Scientific Contributions: Developed novel ANN-based prediction models for RO desalination plants Proposed dynamic control strategies for solar collectors Integrated solar thermal systems with industrial evaporators for decarbonization Applied multi-objective genetic algorithms for techno-economic optimization Anubhav Goel actively collaborates with researchers from Indian Institute of Technology Roorkee and other institutions. His work has been featured in press coverage, indicating public and academic engagement. While no formal advising or grant information is available, his publication record suggests active involvement in funded research projects and collaborative networks.

• Plant-Microbe Interactions
• Microbiome Research
• Sustainable Agriculture
• +۷ مورد دیگر

Dr. Roeland Berendsen is an Assistant Professor and Independent Research Group Leader in the Institute of Environmental Biology at Utrecht University, Faculty of Science. He leads research in Plant-Microbe Interactions and serves as Managing Director of the Netherlands Plant Eco-phenotyping Center (NPEC) Utrecht, a state-of-the-art facility for high-throughput plant phenotyping. Research Interests: His work integrates microbiome sequencing, molecular biology, and data science to understand how plants interact with their microbial communities. Key areas include disease-induced microbiome recruitment, predictive modeling of plant vigor from microbiome data, and the ecological mechanisms behind soil-borne protective legacies. His research aims to develop sustainable, microbiome-based strategies for crop protection and yield improvement. The recent publications reflect a strong trend in leveraging machine learning and advanced data science to decode plant-microbiome interactions, with applications in potato and soybean systems. Themes include predictive microbiome analytics, stress resilience, nitrogen fixation, and field application of protective microbial consortia. Scientific Leadership and Projects: Managing Director, NPEC Utrecht Principal Investigator, NWO Open Technology Program (SPOTM) Lead of Microbiomes4Soy and CropXR Potato Satellite Program Researcher in the MiCRop Gravitation Program Advising and Grants: Dr. Berendsen supervises multiple PhD candidates and postdoctoral researchers across several funded projects. His research is supported by national and international grants focusing on sustainable agriculture, microbiome engineering, and climate-resilient crops. He mentors a dynamic team working on cutting-edge solutions for future food security. Labs and Teams: He leads the Plant-Microbe Interactions group at Utrecht University and oversees operations at NPEC Utrecht, which provides advanced phenotyping capabilities to academic and industry partners.

Ali Mohammadi-Gheidariمشاهده پروفایل

دانشیار

• Charge Particle Optics
• MEMS electron optics
• Multi electron beam systems
• +۳ مورد دیگر

Ali Mohammadi-Gheidari is an Associate Professor of Imaging Physics at the Faculty of Applied Sciences, Delft University of Technology (TU Delft) since 2024. Previously, he served as an Affiliated Assistant Professor of Imaging Physics (2019-2024) and as a Part-Time Scientist of Imaging Physics (2016-2019). He has maintained a dual career as a Scientist at Thermo-Fisher (formerly FEI) in Eindhoven from 2011 to 2024. He completed his PhD in Physics from the Imaging Physics Department at TU Delft between 2007 and 2011. Dr. Mohammadi-Gheidari's research focuses on Charge Particle Optics (theory and instrumentation), MEMS electron optics for cost-effective electron microscopy components, Multi electron beam systems , and Quantum Electron Microscopy (QEM) concepts. His work bridges theoretical physics with practical instrumentation development, with applications spanning materials science, semiconductor manufacturing, and advanced microscopy techniques. His recent publications demonstrate a strong focus on multi-beam system interactions, optimization of electron optics using genetic algorithms, and novel charged particle beam technologies. The research shows a clear progression toward more efficient, high-resolution microscopy solutions with practical industrial applications, particularly in semiconductor inspection and materials analysis. Dr. Mohammadi-Gheidari serves as a peer reviewer for the Journal of Vacuum Science and Technology. Part B: Microelectronics and Nanometer Structures, contributing to scholarly evaluation in his field. His collaborative work with researchers like Pieter Kruit, E.R. Kieft, and Cornelis Wouter Hagen demonstrates strong academic and industry connections. At TU Delft, he teaches courses including "Systems Engineering for...", "Introduction to Charged...", "Inleidend Practicum" series, and "Physics of Electron...". His teaching portfolio reflects his expertise in electron physics and systems engineering, preparing students for careers at the intersection of physics and advanced instrumentation.

• Artificial Intelligence
• Computer Science
• Genomics
• +۱۱ مورد دیگر

Nikolaos Alachiotis is an Associate Professor at the Digital Society Institute, specializing in Artificial Intelligence, Field-Programmable Gate Arrays (FPGA), and Genomics. His work bridges Hardware Acceleration with Bioinformatics , focusing on scalable solutions for genomic analysis and evolutionary biology. Research Themes: AI Hardware, FPGA Optimization, Population Genetics Key Collaborations: Delft University of Technology, Zenodo, Frontiers in High-Performance Computing Research Focus Alachiotis develops deep learning and FPGA-based tools for selective sweep detection and phylogenetic inference , enabling faster genomic analyses. His work spans convolutional neural networks , spiking networks , and adaptive computing for bioinformatics and radio astronomy. Recent articles highlight trends in AMD Versal SoC applications, deep learning for natural selection , and scalable genomic pipelines . Scientific Awards Outstanding Student Paper Award (2023) Supervised Work His supervised projects include Accelerating Selective Sweep Detection and Effective Data Preprocessing Techniques , often in collaboration with institutions like Zenodo and Frontiers journals.

Amin Asadiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Autonomous Agent Systems
• Mobility and Transport
• +۶ مورد دیگر

Amin Asadi is an Assistant Professor in Data Science, Artificial Intelligence, Operations Research, and Logistics at the University of Arkansas, specializing in Industrial Engineering & Business Information Systems. His research focuses on machine learning applications for complex systems and optimization challenges. PhD in Industrial Engineering from University of Arkansas Research interests span multiple domains including: Green AI and sustainable computing Drone delivery optimization Healthcare logistics Pharmaceutical cold chain management Electric vehicle infrastructure Stochastic decision-making models His work contributes to the UN Sustainable Development Goals, particularly in energy efficiency and healthcare accessibility. Key publication trends show: 70% focus on transportation/logistics optimization 45% integration of machine learning with operations research 30% sustainability applications 25% healthcare system improvements Recent outputs demonstrate expertise in simulation modeling, optimization algorithms, and data-driven decision systems with industry applications.

Mark Hoogendoornمشاهده پروفایل

استاد

• Machine Learning
• Healthcare Technology
• Predictive Modeling
• +۳ مورد دیگر

Prof. Mark Hoogendoorn is a Full Professor at the Vrije Universiteit Amsterdam (VU) in the Faculty of Science, Department of Artificial Intelligence. He is also affiliated with the Network Institute. His research focuses on machine learning applications in healthcare, including predictive modeling for diseases, personalized therapies, and eHealth systems. He holds roles as Director/Owner in Cairus Holding B.V. and PersonalAIze B.V., and is a Board Member of Volleybalvereniging Waterlanders. His expertise aligns with UN Sustainable Development Goals, particularly in improving health and well-being. He has supervised 13 PhD theses and teaches courses like Data Mining Techniques and Machine Learning for the Quantified Self. He is an editorial board member of Applied Artificial Intelligence journal. Recent publications highlight contributions to machine learning in healthcare, telecommunication systems, and reinforcement learning. His work emphasizes explainability, safety, and hybrid intelligence in AI. Key areas include mortality prediction in pediatric ICU patients, cardiac arrest risk in diabetes, and signal processing innovations.

Herm Hofmeyerمشاهده پروفایل

دانشیار

• Structural Design
• Finite Element Method
• Topology Optimization
• +۵ مورد دیگر

Hèrm Hofmeyer is an Associate Professor in the Department of the Built Environment at TU/e, specializing in Computational Structural Design and Finite Element Method applications. He holds a PhD in Applied Mechanics/Steel Structures and has supervised over 89 theses, including 9 PhD and 2 PDEng projects. His work focuses on topology optimization, fire-structure interactions, and sustainable construction methods. Education: MSc in Computer Aided Structural Design/Applied Mechanics (1994), PhD in Applied Mechanics/Steel Structures (2000). He has been a visiting scholar at Universität Stuttgart and RWTH Aachen. Research Interests: Structural Design Automation Fire Safety Engineering 3D Printing in Construction Topology Optimization Finite Element Modeling Steel and Sandwich Panel Structures Awards: Recipient of TU/e's Best Lecturer awards (BSc 2019, MSc 2022) and EG ICE 2016 Best Paper Award. Active in academic service as a reviewer for journals and conference committees. Labs/Teams: Member of EAISI (Eindhoven Artificial Intelligence Systems Institute), ECCS-TC7 (European Convention for Constructional Steelwork), and CIB-W78 (International Council for Research and Innovation in Building and Construction).

• Systems Biology
• Metabolic Pathways
• Cell Biology
• +۳ مورد دیگر

Johan van Heerden is an Assistant Professor in the Faculty of Science at Vrije Universiteit Amsterdam, holding dual appointments in Systems Biology and the AIMMS research institute. His research focuses on understanding metabolic pathways, cell growth dynamics, and microbial physiology, particularly in yeast systems. His work contributes to UN Sustainable Development Goals, emphasizing sustainable resource allocation and metabolic strategies. He teaches advanced courses including Biochemistry Research, Systems Biology, and Microbiology, reflecting his expertise in experimental and computational biology. Research interests include yeast metabolism, proteome constraints, and single-cell analysis. His recent publications highlight topics like pH dependencies in glycolytic enzymes, metabolic modeling in fission yeast, and the evolution of cell size in Saccharomyces cerevisiae. Collaborations span international teams, with active projects on bacterial cytoplasm organization and cAMP signaling mechanisms. He has supervised one PhD thesis and maintains no ancillary activities beyond academic commitments.

• Artificial Intelligence
• Metaheuristic Optimization
• Evolutionary Algorithms
• +۳ مورد دیگر

Daan van den Berg is a Lecturer in the Faculty of Science at Vrije Universiteit Amsterdam, with a focus on Artificial Intelligence. He is also affiliated with the Network Institute. His research interests include metaheuristic optimization, evolutionary algorithms, and computational intelligence. Recent work focuses on improving benchmark testing for algorithms, protein conformation sampling, and fractal analysis of computational problems. He has collaborated internationally on projects involving hybrid algorithms and entropy analysis. Education details are not explicitly provided in the text. His research output includes 22 publications, with recent works appearing in Springer Nature and conference proceedings like IJCCI and PPSN. He has contributed to studies on algorithm performance, problem instance complexity, and explainable optimization techniques. No scientific awards are listed. His advising and grant activities are unspecified in the text. He is actively involved in computational intelligence research groups and has collaborated across disciplines including bioinformatics and mathematics.

Klaas Slootenمشاهده پروفایل

استاد

• Forensic Genetics
• Probability and Statistics
• DNA Analysis
• +۳ مورد دیگر

Klaas Slooten is a Professor of Mathematics for Forensic Genetics at Vrije Universiteit Amsterdam (VU) and works at the Netherlands Forensic Institute (NFI). He holds a dual affiliation combining academic and applied forensic expertise. His research focuses on statistical methods in forensic genetics, particularly DNA mixture analysis, likelihood ratio modeling, and familial searching strategies. Education: PhD in Mathematics from the University of Amsterdam (2003), specializing in algebraic structures related to Hecke algebras. Earlier academic work included studies on representation theory and combinatorial generalizations of Springer correspondence. Research interests include probabilistic evaluation of DNA evidence, computational tools for forensic analysis (e.g., MixKin software), and Bayesian network applications in victim identification (e.g., Bonaparte software). He teaches advanced courses on forensic probability and statistical evidence evaluation at VU and collaborates internationally on forensic genetics projects. Notable contributions include methodologies for interpreting complex DNA mixtures and improving familial database search strategies. Key achievements include developing the MixKin software for DNA mixture analysis and presenting at major conferences like the ISFG World Congress. His work bridges theoretical mathematics with practical forensic applications, emphasizing rigorous statistical frameworks for legal evidence interpretation.

• Radiation Therapy
• Medical Imaging
• Oncology
• +۳ مورد دیگر

Manouk van Acht is a Researcher at the Department of Radiotherapy of Erasmus MC . Their work focuses on advancing radiation therapy techniques, medical imaging innovations, and oncology treatments. Key areas include AI-driven synthetic CT imaging for palliative care, optimization of radiotherapy workflows, and clinical analyses of re-irradiation outcomes for bone metastases. Expertise spans radiation oncology, medical algorithms, and clinical trial analysis Published 15 peer-reviewed articles with recent contributions on AI in medical imaging and workflow optimization Active in multidisciplinary collaborations across institutions Research emphasizes improving patient outcomes through technical advancements in radiotherapy delivery systems and evidence-based clinical practices.

Matthias Heinemannمشاهده پروفایل

استاد

• Microbial Metabolism
• Systems Biology
• Single Cell Analysis
• +۶ مورد دیگر

Matthias Heinemann is a Professor of Molecular Systems Biology at the University of Groningen, Netherlands, affiliated with the Faculty of Science and Engineering. He leads the Molecular Systems Biology Group, focusing on microbial metabolism, single-cell analysis, and systems biology. His work bridges biochemistry, microbiology, and computational modeling to understand cellular decision-making and metabolic processes. He earned his PhD in biochemical engineering from RWTH Aachen University (2003) and held positions at ETH Zurich before joining the University of Groningen in 2010 as an Associate Professor, becoming a Full Professor in 2013. He is a leader in the Groningen Biomolecular Sciences and Biotechnology Institute (GBB) and has contributed to initiatives such as the EU ITN MetaRNA project. Key research areas include metabolic flux sensing, cell cycle-metabolism coupling, and antibiotic persistence mechanisms. His lab developed technologies like the PunctaFinder algorithm and single-cell metabolomics methods. Heinemann has received prestigious awards including the VICI (2020), VIDI (2011), and DuPont Young Professor Awards. He serves on editorial boards (e.g., Current Opinion in Biotechnology) and advisory roles in industry and academia. His work addresses global challenges like improving photosynthetic efficiency and sustainable laboratory practices.

Ernst Witمشاهده پروفایل

استاد

• Network Modelling
• High-dimensional Inference
• Statistical Genomics
• +۴ مورد دیگر

Ernst Wit is a Professor of Statistics and Probability at the University of Groningen, affiliated with the Johann Bernoulli Institute. He holds a PhD in Philosophy from Pennsylvania State University (1997) and a PhD in Statistics from the University of Chicago (2000). His research focuses on network science, high-dimensional inference, systems biology, and algebraic statistics, with applications in genetics and ageing research. He has led large-scale projects, including coordinating a Europe-wide network on Network Science through an EU COST Action. Wit has supervised over 12 PhD students and numerous postdocs, contributing to advancements in statistical methodology and its interdisciplinary applications. Wit’s academic roles include Chair of the Board at the Johann Bernoulli Institute and former President of the Biostatistics Netherlands Society. His work bridges philosophy and science, with interests in game theory, ethics, and Kantian philosophy. Key achievements include challenging assumptions about scale-free networks and developing statistical frameworks for genomic data analysis. His research often addresses methodological gaps in bioinformatics and systems biology, emphasizing rigorous statistical approaches. Publications span statistical theory, bioinformatics, and applied mathematics, reflecting his interdisciplinary approach. Awards include the Fulbright Fellowship and grants from NWO and the UK’s Natural Environment Research Council. Current efforts focus on dynamic network inference, ODE modeling in biological systems, and statistical methods for museum visitor behavior analysis (MUSE project).

• Agricultural Robotics
• Digital Agriculture
• Computer Vision
• +۴ مورد دیگر

Selwin Hageraats is a Researcher at Wageningen University & Research, specializing in Robotics and Automation within greenhouse horticulture. His work focuses on integrating computer vision, digital twin technology, and automated phenotyping to optimize crop cultivation. Research Interests His research spans agricultural robotics, digital agriculture, and plant science, with specific contributions to: Autonomous farming platforms and precision horticulture Computer vision for crop monitoring and phenotyping Digital twin applications in tomato cultivation Non-invasive measurement techniques for plant physiology Deep learning and sensor fusion in agricultural systems Technological solutions for greenhouse pest and disease detection Research Output Active in developing automated systems for tomato growth analysis and perennial ryegrass phenotyping, Hageraats has pioneered camera-based parameter tracking and mobile segmentation algorithms. His work bridges theoretical models with practical greenhouse automation, emphasizing data-driven cultivation strategies.

• Genomics
• Breeding Value
• Pedigree
• +۴ مورد دیگر

Jeremie Vandenplas is a Researcher at Animal Breeding & Genomics , affiliated with Wageningen University & Research . His work focuses on genomic prediction, genetic evaluation, and the impact of environmental factors like heat stress on livestock traits. Fields of Interest : Genomics, Breeding Value, Pedigree, Beef Cattle, Single Nucleotide Polymorphism, Dairy Breeds Recent research includes quantum computing applications in agriculture , heat stress effects on fertility and methane emissions in dairy cows, and multi-breed genomic prediction models . His activities span international collaborations with institutions like the European Federation of Animal Science (EAAP). He leads projects such as EU-21001 RUMIGEN and LWV23062, focusing on genetic variability, climate resilience, and data integration. His supervised work includes PhD candidates Bonifazi, Aivazidou, and collaborators ten Napel, Veerkamp, and Calus.