جستجوی استاد راهنمای دانشگاهی

Melih Kandemir is an Associate Professor at the Department of Mathematics and Computer Science, Southern Denmark University. He also serves as Research Group Leader at the Bosch Center for Artificial Intelligence (2018–2021) and held a previous role as Assistant Professor at Ozyegin University (2017–2018). His research focuses on machine learning, Bayesian methods, reinforcement learning, and uncertainty quantification. **Education**: PhD in Computer Science from Aalto University (2013), specializing in 'Learning Mental States from Biosignals'. **Research Interests**: Machine Learning, Bayesian Inference, Reinforcement Learning, Deep Neural Networks, Stochastic Processes. His work emphasizes theoretical foundations and practical applications in domains like medical imaging, control systems, and robotics. **Awards**: Two Best Paper Awards (2017). **Grants & Projects**: Includes the Carlsberg Young Researcher Fellowship (2022–2026), Novo Nordisk Foundation grants (2021–2024), and DFF-funded research on PAC-Bayesian reinforcement learning (2025–2027). **Labs/Teams**: Leads research on Bayesian deep learning and reinforcement learning within the Bosch Center for AI and SDU's interdisciplinary groups.

• Machine Learning
• Reinforcement Learning
• Bayesian Inference
• +۳ مورد دیگر

Faisal Mahmood is a Professor at the Department of Clinical Research, University of Southern Denmark (SDU), with dual affiliations at Odense University Hospital (OUH). He is a key member of the Research Unit of Oncology and the AgeCare - Academy of Geriatric Cancer Research in Odense, where he leads advanced research in imaging biomarkers for radiotherapy response. His work bridges clinical oncology and medical physics, with a focus on improving cancer treatment through innovative imaging techniques. Research Interests: Dr. Mahmood's research centers on imaging biomarkers of response to radiotherapy , with expertise in radiation therapy , medical image processing , and diffusion MRI . His work explores tumor microstructure using time-dependent diffusion imaging and MRI-Linac systems, with applications in glioblastoma and pancreatic cancer. He is actively involved in developing low-dose, adaptive radiotherapy protocols to enhance treatment precision. The recent trend in his publications shows a strong focus on adaptive radiotherapy , quantitative MRI , and biologically guided treatment . His work integrates engineering principles with clinical oncology, emphasizing reproducibility and feasibility in clinical settings. Topics such as automatic beam gating, diffusion coefficient discrepancies across scanners, and histological validation of imaging biomarkers reflect his translational research approach. Scientific Awards and Recognition: Research supported by Knæk Cancer foundation Advising and Grants: Dr. Mahmood has supervised at least 3 academic works, including PhD-level research. His projects are supported by institutional and external funding, notably from cancer research foundations. He actively collaborates with multidisciplinary teams across Denmark and internationally, contributing to both national and global oncology research initiatives. Labs and Research Teams: He is affiliated with the Research Unit of Oncology and the AgeCare - Academy of Geriatric Cancer Research at OUH, where he contributes to cutting-edge research in geriatric oncology and advanced radiotherapy. His team integrates clinical data, imaging physics, and machine learning to develop personalized treatment strategies for cancer patients.

• Medical Image Processing
• Radiation Therapy
• Imaging Biomarkers
• +۷ مورد دیگر

Kim Bjerge serves as Associate Professor and Group Leader in Aarhus University's Department of Electrical and Computer Engineering, specializing in computer vision and machine learning applications for ecological monitoring. His research bridges engineering and environmental science to develop innovative solutions for insect biodiversity assessment and sustainable agriculture. His core research interests include computer vision, deep learning, and edge computing systems for real-world ecological monitoring. Dr. Bjerge develops time-lapse camera pipelines and deep learning models specifically for insect population tracking in natural environments, with emphasis on agricultural applications like black soldier fly farming and biodiversity conservation. His work integrates signal processing techniques with biological data to create field-deployable monitoring systems. Recent publications reveal a strong trend toward practical implementations of computer vision in entomology, particularly focusing on edge processing for camera traps, automated trait prediction in insect farming, and biodiversity monitoring systems. Key research areas include nocturnal insect monitoring, floral environment analysis, and developing specialized datasets like AMI for insect identification in wild settings. He leads multiple significant research projects funded through competitive grants: MAMBO: Modern Approaches to Monitoring Biodiversity (2022-2026) FLYgene: Sustainable Insect Production for Livestock Feed (2022-2026) Automatisk monitering af nataktive insekter: Automatic nocturnal insect monitoring (2024-2029) Pilotprojekt for automatisk registrering af invasive plantearter: Invasive species monitoring (2020-2021) As head of the Signal Processing and Machine Learning research group, Dr. Bjerge directs interdisciplinary teams developing computer vision solutions for biological monitoring systems. His laboratory focuses on creating robust field-deployable technologies including scanner-based arthropod imaging systems, time-lapse camera networks for floral environments, and edge AI processors for real-time insect monitoring in agricultural settings.

• Computer Vision
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Ole Winther is a Professor at the Department of Biology, University of Copenhagen, specializing in Computational and RNA Biology. He also holds a joint appointment as Professor at DTU Compute, Technical University of Denmark. His research bridges machine learning, bioinformatics, and natural language processing with applications in biological sequence analysis, transcriptomics, and health informatics. Education: 1998: PhD in Physics, University of Copenhagen 1994: Master of Science in Physics, University of Copenhagen Winther's research focuses on developing advanced machine learning methodologies for biological applications. He has pioneered protein language models for sequence analysis (DeepLoc, SignalP, DeepTMHMM), interpretable deep learning for RNA subcellular localization, and benchmarking frameworks for DNA language models. His work spans latent variable models, variational inference, diffusion models, and novel architectures for deep generative modeling, with increasing emphasis on practical healthcare applications including rare disease diagnosis through findzebra.com and medical question answering with large language models. Scientific Recognition: ELLIS Fellow (2021) Head of ELLIS Copenhagen Unit H-index of 61 (Google Scholar, May 2023) 19,700+ citations (Google Scholar, May 2023) Winther has supervised 25+ PhD students to completion with 7 currently in progress, along with over 100 master's projects. He frequently serves as PhD opponent and committee chairman across European institutions. His research is supported by substantial funding including multiple Novo Nordisk Foundation grants totaling over 60 million DKK for the Center for Basic Machine Learning Research in Life Science and CAZAI projects, plus significant funding from the Danish Independent Research Fund. He leads an active research group developing cutting-edge machine learning approaches for bioinformatics and NLP challenges. Winther co-founded two spin-out companies: findzebra.com (2014, 2018), a search engine for rare diseases, and raffle.ai, an NLP startup for enterprise search. He initiated DTU's popular BSc in AI and Data program and teaches the highly enrolled MSc course in Deep Learning (450+ students) and PhD course in Bayesian Data Analysis.

• Machine learning methods development
• Bioinformatics
• Biological sequence analysis
• +۸ مورد دیگر

Lisbeth Truelstrup Hansen is a Professor and Head of the Research Group for Food Microbiology and Hygiene at the National Food Institute, Technical University of Denmark (DTU), where she leads research in microbial food safety, environmental hygiene, and pathogen survival mechanisms. She is also affiliated with the DTU Microbes Initiative and actively contributes to projects addressing food safety, Arctic sanitation, and public health. University: Technical University of Denmark School: National Food Institute Department: Research Group for Food Microbiology and Hygiene Role: Professor and Research Group Head Her research focuses on microbial food safety, with particular emphasis on Listeria monocytogenes , biofilm formation, hygiene in food production, and innovative preservation and detection methods. She also investigates water and environmental hygiene, including sewage management in Arctic regions under climate change. Her work supports UN Sustainable Development Goals related to clean water, sanitation, and good health. The recent publications highlight a strong trend in applying genomics, machine learning, and predictive modeling to food safety challenges. Her team investigates disinfectant resistance in Listeria , growth modeling of Bacillus cereus , and quality assessment in seafood and processed foods, demonstrating a multidisciplinary approach combining microbiology, environmental science, and data science. Scientific Contributions: Lead researcher in multiple national and international projects Active contributor to food safety policy and public discourse Regular media and conference speaker on hygiene and food safety topics Lisbeth Truelstrup Hansen supervises several PhD students and collaborates across disciplines on grants related to foodborne pathogens, climate impact on water quality, and sustainable food production. She has led or co-led projects such as 'ArcticSewlution' and 'ILLUQ. PERMAFROST – POLLUTION - HEALTH', which integrate environmental engineering with microbiology for public health outcomes. She is involved in key research labs and teams including the Research Group for Food Microbiology and Hygiene and the DTU Microbes Initiative, which focus on pathogen detection, biofilm dynamics, and hygiene solutions for the food industry.

• Food Microbiology
• Microbial Food Safety
• Hygiene in Food Production
• +۱۳ مورد دیگر

Zorana Jovanovic Andersen is an Associate Professor at the University of Copenhagen, Department of Public Health, Section of Environmental Health, specializing in Environmental and Occupational Epidemiology. Her research focuses on health impacts of air pollution , noise exposure , and lifestyle factors across large cohorts including the Danish Nurse Cohort and Diet, Health, and Cancer cohort. Primary research areas: Environmental Epidemiology, Breast Cancer Risk, Air Pollution, Noise Studies, Physical Activity Interactions Key projects: Hyperion (road traffic noise), ELAPSE (low-level air pollution), TAPAS (climate-transportation health risks) Research Themes She investigates long-term exposure to air pollution and noise in relation to breast cancer , diabetes , cardiovascular disease , and brain cancer , while exploring how factors like physical activity modify these risks. Recent work includes dementia and COVID-19 mortality analyses. Scientific Contributions Her 15 most recent publications address: 1) Air pollution disparities in Europe 2) Dementia-air pollution links 3) Stroke-noise interactions 4) Airport ultrafine particle exposure 5) Asthma-COPD overlap 6) Breast cancer-mammographic density relationships. Major Grants Danish Working Environment Research Fund (1.79M DKK, 2017) Danish Heart Association (433K DKK, 2016) Danish Council for Independent Research (1.93M DKK, 2015) Teaching & Leadership Course Leader for Data Processing in Health Care and Public Health Informatics at UCPH since 2011. Organized Danish Health Registries seminars and served on scientific committees for environmental conferences.

• Environmental Epidemiology
• Air Pollution
• Noise Pollution
• +۱۲ مورد دیگر

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +۳ مورد دیگر

Henrik Myhre Jensen is a Professor at the College of Engineering , Aarhus University, specializing in Mechanics of Materials , Solid Mechanics , and Mechanical Engineering . His research focuses on fracture mechanics, composite materials, and computational modeling of structural behaviors. Research Focus Fracture mechanics in composites and layered materials Computational modeling of kink band propagation Surface wear and coating technologies Ultrasound imaging applications in mechanical systems Notable Contributions Henrik has contributed to understanding crack propagation in cantilever beams, developed numerical methods for simulating delamination in composites, and explored buckling instabilities in solids. His recent work connects machine learning (holomorphic neural networks) to traditional fracture mechanics problems. Key Projects MAGFLY (2017-2021): Magnets for Flywheel Energy Storage InnoVacc (2009): Pressure Testing of Vacuum Chambers Simulation of composite structures (2011-2020): Micro-mechanical modeling

• Mechanics of Materials
• Solid Mechanics
• Mechanical Engineering
• +۴ مورد دیگر

Charles Marcus is a Professor at the University of Copenhagen's Niels Bohr Institute, holding the Villum Kann Rasmussen Chair in Quantum Sciences. He directs the Center for Quantum Devices and Microsoft Station Q – Copenhagen, while affiliating with the Niels Bohr International Academy. Education : Stanford University (B.S. 1984), Harvard University (Ph.D. 1990), IBM Postdoctoral Fellow (1990-92) Employment : Faculty at Stanford (1992-2000), Harvard (2000-2011), and UCPH (2012-present) His research focuses on experimental condensed matter physics, particularly quantum coherent electronics in semiconductors/superconductors. Key areas include spin qubits for quantum computing, Majorana modes in nanowires, quantum Hall systems, and superconductor-semiconductor hybrids. Recent work explores topological quantum information schemes and novel magnetic resonance imaging approaches. Scientific publications span quantum devices, Josephson junctions, and topological materials. Awards include the H.C. Ørsted Gold Medal, AAAS Newcomb-Cleveland Prize, and fellowships from AAAS and APS. He serves on advisory boards for quantum technology centers globally. Significant Awards : H.C. Ørsted Gold Medal (2020) Industry Prize, Danish Academy of Natural Sciences (2019) Member, National Academy of Sciences (2018) Award for Research Excellence in Nanotechnology (2014) Professional Roles : Director, Center for Quantum Devices (2012-2019) Lab Director, Microsoft Quantum (2016-2021) Scientific Director, Harvard Center for Nanoscale Systems (2004-2009)

• Condensed Matter Physics
• Quantum Computing
• Quantum Coherent Electronics
• +۴ مورد دیگر

Borja Valverde Pérez is an Associate Professor in the Department of Environmental and Resource Engineering at the Technical University of Denmark (DTU). His research advances sustainable water resource recovery through biotechnology innovations. Research focuses on microalgae cultivation, microbial protein production, and bioelectrochemical systems for water treatment. Key projects explore nutrient recovery from waste streams and greenhouse gas mitigation in biological processes. Recent publications demonstrate innovative approaches to wastewater valorization, including microalgae-bacteria consortia for pollutant degradation and inorganic bioelectric systems for low-temperature denitrification. Work integrates experimental validation with life cycle assessment. He coordinates projects on industrial symbiosis and circular economy applications in water systems. Current initiatives investigate decarbonization pathways for water resource recovery facilities through energy-flexible operation.

• Microalgae Biotechnology
• Resource Recovery
• Life Cycle Assessment
• +۳ مورد دیگر

Mark Bo Jensen is an Assistant Professor (tenure track) at the Department of Engineering Technology and Didactics, Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research is centered on Perception Engineering and Extended Reality technologies, particularly Virtual Reality (VR), with applications in human cognition, computer graphics, and scientific visualization. His research interests lie at the intersection of engineering and cognitive sciences, focusing on creating immersive and convincing extended reality experiences. Jensen applies his over 10 years of expertise in real-time computer graphics to advance VR systems for perception modeling, geometric data visualization, and material appearance simulation. His work contributes to fields such as medical diagnostics, 3D annotation, and photorealistic rendering. The recent publications highlight a strong trend in leveraging VR for scientific tasks, such as anatomical landmark annotation and visual field testing, as well as advancing core graphics techniques like meshlet optimization and diffusion-based stereo image generation. His research integrates computer vision, graphics algorithms, and human-centered design. While no scientific awards are currently listed, his active participation in research projects and consistent publication output indicate a growing academic profile. He has contributed to interdisciplinary collaborations involving medical, biological, and engineering domains. Jensen has been involved in advising and research projects, including serving as a PhD student in the 'Virtual Reality-Based Visualization of Geometric Data' project and currently as a project participant in 'AL-EYE: The Visual Aid'. These projects reflect his focus on applied VR solutions and data understanding. His work is conducted within the Energy Technology and Computer Science division at DTU, where he contributes to advancing perception-driven technologies and their practical implementation in scientific and medical contexts.

• Perception Engineering
• Extended Reality
• Virtual Reality
• +۷ مورد دیگر

Luka Radic is a Researcher in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His work bridges theoretical and applied research in machine learning, with a focus on quantum machine learning , large language models , and fairness in AI systems.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +۷ مورد دیگر

Shashi Raj Pandey serves as Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, Denmark. His research is anchored in the Connectivity section and Connectivity Classique-Center for Classical Communication in the Quantum Era, with office location at Fredrik Bajers Vej 7C, C1-111, 9220 Aalborg Øst. His core research spans Network Economics, Game Theory, and Wireless Networks, with specialization in Decentralized Machine Learning and Semantic/Goal-oriented Communications. Current work integrates Digital Twin technologies with 6G systems for industrial automation and earth observation, emphasizing resource-efficient protocols for Internet of Things and edge intelligence applications. Recent publications (2024-2025) reveal a clear trajectory toward AI-6G convergence, featuring semantic communications for satellite imaging, game-theoretic network resource allocation, and digital twin implementations for autonomous systems. Key themes include communication efficiency in distributed learning and physical-digital world integration. Notable recognitions include: Best PhD Thesis Nominee (2021) Excellent Paper at Korea Software Congress, KIISE 2021 Student Best Paper Award at APNOMS 2019 Best Paper at Korea Software Congress, KIISE, 2018 Brain Korea 21st Century Plus Fellowship Academic service includes external PhD examination for EU SNS projects and peer review for premier conferences (AAAI, ICLR, ICML). His lab work within the Connectivity Classique-Center explores classical communication frameworks applicable to quantum-era networks, with focus on semantic information theory and decentralized network architectures.

• Network Economics
• Game Theory
• Wireless Networks
• +۷ مورد دیگر

Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +۵ مورد دیگر

Poul Henning Jensen is a full Professor at Aarhus University , affiliated with both the Department of Biomedicine (Skou-building) and the Department of Molecular Biology and Genetics (Neurobiology section). Research Interests : Neurobiology of synucleinopathies Molecular mechanisms in Parkinson's disease Protein aggregation and propagation Calcium signaling in neurodegeneration Neuroinflammatory responses Scientific Activities : His work focuses on α-synuclein biology, covering structural analysis, aggregation pathways, and their pathological consequences. Recent studies include novel detection methods for early α-synuclein pathology mechanisms of neurotoxicity sex-specific neuroprotective factors and protein interaction networks in disease models . Scientific Awards : Recipient of at least one significant research prize Collaborations : Works with international teams on clinical trials targeting α-synuclein and coordinates multiple studies on neurodegenerative disease mechanisms.

• Neurobiology
• Parkinson's Disease
• Synucleinopathies
• +۴ مورد دیگر