جست‌وجوی استاد و پژوهشگر

• Anesthesiology
• Perioperative Medicine
• Emergency Medicine
• +۳ مورد دیگر

Morten Vester-Andersen serves as a Clinical Associate Professor in the Department of Clinical Medicine, specifically within the Anaesthesiology section at the University of Copenhagen's Faculty of Health and Medical Sciences. His institutional affiliation is with Region Hovedstaden (Capital Region of Denmark), where he conducts clinical research alongside his academic responsibilities in Copenhagen, Denmark. Dr. Vester-Andersen's research spans several critical areas in perioperative medicine, with particular focus on anesthesiology protocols , diagnostic imaging applications in emergency settings , and perioperative complications management . His work frequently examines the intersection of imaging technology and clinical decision-making, particularly regarding CT scans and MRI in emergency and surgical contexts. Recent publications demonstrate significant contributions to understanding perioperative hypotension, delirium assessment tools, and the application of artificial intelligence in diagnostic imaging. His research output shows strong collaborative patterns across Danish medical institutions, with numerous co-authored publications involving colleagues from multiple departments. The trend in his recent work indicates growing interest in the application of artificial intelligence and advanced imaging techniques to improve diagnostic accuracy and patient outcomes in emergency and perioperative settings. Dr. Vester-Andersen actively participates in clinical research consortia such as the Collaboration for Evidence-based Practice and Research in Anaesthesia (CEPRA), demonstrating his commitment to evidence-based approaches in anesthesiology practice. His work spans both clinical investigations and methodological reviews that shape best practices in the field. His research has attracted attention across academic platforms, with multiple publications receiving citations and mentions in news outlets. Several of his studies have been highlighted for their methodological rigor and clinical relevance to improving patient safety and outcomes in anesthesia and emergency care settings.

Tibor V Vargaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Algorithmic Fairness
• +۱۰ مورد دیگر

Tibor V Varga is an Associate Professor at the Department of Public Health, University of Copenhagen, where he is affiliated with the Copenhagen Health Complexity Center. His work focuses on applying artificial intelligence and machine learning techniques to address health equity issues through analysis of life-course big data. Education: PhD in Genetic Epidemiology, Lund University (2016) MSc in Nutritional Sciences, Semmelweis University (2013) BSc in Biology, Eotvos Lorand University (2009) Tibor's research spans the intersection of artificial intelligence, epidemiology, and social determinants of health. His primary areas of interest include machine learning, predictive statistics, causal inference, and biomarker analysis with a focus on addressing social inequalities and promoting health justice. He has particular expertise in applying algorithmic fairness principles to health data analysis, ensuring that AI-driven health solutions do not exacerbate existing health disparities. His work often involves large-scale data analysis to identify patterns that can inform public health interventions aimed at reducing health inequities across diverse populations. His recent publications demonstrate a clear trajectory toward using AI to address complex health equity challenges. The articles span diverse topics including racial disparities in diabetes technology, psilocybin therapy for alcohol use disorder, European health data infrastructure, and the relationship between digital behaviors and health outcomes. This breadth reflects his interdisciplinary approach, combining computational methods with public health applications to tackle issues at the intersection of technology, society, and health. Tibor actively contributes to methodological discussions in scientific publishing and has co-authored works on improving the efficiency of biomedical publishing processes. His research has received attention in multiple news outlets and academic platforms, indicating the relevance and impact of his work in both academic and public spheres. His collaborative network extends across Europe and beyond, working with researchers from various disciplines to address complex health challenges through data-driven approaches. This international collaboration is particularly evident in his work related to the European Health Data Space initiative.

Christian Beltoft Brøchnerمشاهده پروفایل

دانشیار بالینی

• Neuroscience
• Developmental Biology
• Stem Cell Research
• +۵ مورد دیگر

Christian Beltoft Brøchner is a Clinical Associate Professor at the Department of Clinical Medicine, University of Copenhagen. His academic career spans both clinical medicine and research, with a focus on human brain development and stem cell research. Faculty: Faculty of Health and Medical Sciences, University of Copenhagen Department: Department of Clinical Medicine Academic Rank: Clinical Associate Professor Dr. Brøchner completed his Bachelor of Medicine (2002-2005), MD (2009), and has been pursuing PhD studies at the University of Copenhagen since 2011. His clinical background includes work as a General Practitioner and internships in Orthopedics and Neurology. His research centers on the developing human neocortex with particular focus on glycoprotein YKL-40, which appears in human embryonic stem cells, normal brain development, cancer forms, and inflammatory diseases. He also investigates stem cell differentiation compared to normal human development. Dr. Brøchner's 18 research outputs demonstrate expertise across neuroscience, developmental biology, cancer research, and immunology. His 2019 paper on astrogliogenesis has received significant attention with 55 Scopus citations, indicating substantial impact in the field. Highly cited work on brain development and cancer mechanisms Interdisciplinary research bridging clinical medicine and basic science Methodological expertise in microscopy and statistical analysis He serves as a Board member in the DASCS research network since 2012, demonstrating commitment to collaborative research. His work has been referenced in policy sources and discussed across social media platforms including X, Facebook, and academic networks like Mendeley. Proficient in Danish (fluent), English (fluent), and French (moderate), Dr. Brøchner maintains international research connections while contributing to the academic community through teaching and research supervision.

• Genomics
• Medical Informatics
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Peter Christoffer Holm is a Guest Researcher at the Novo Nordisk Foundation Center for Protein Research within the Faculty of Health and Medical Sciences at the University of Copenhagen. He is affiliated with the Brunak Group and maintains an active research profile with numerous publications in high-impact journals. His work spans multiple disciplines at the intersection of genomics, artificial intelligence, and clinical medicine. Dr. Holm's research interests focus on applying computational approaches to biomedical challenges. His work encompasses genomic analysis of population health data, AI-driven medical record mining for disease detection, cardiovascular disease stratification, and cancer risk assessment. He has made significant contributions to understanding genetic variants affecting body mass index across diverse populations and developing novel methods for patient subgrouping in heart disease. His publication record shows a strong trend toward integrating artificial intelligence with clinical data, particularly in cancer detection and cardiovascular disease management. Recent work demonstrates expertise in leveraging national health datasets for predictive modeling, with publications appearing in prestigious journals including Nature Communications, The Lancet family of journals, and BMC Medicine. His research has garnered significant attention, with multiple papers covered by news outlets and widely shared on social media platforms. As a member of the Brunak Group at the Center for Protein Research, Dr. Holm contributes to a collaborative environment focused on computational biology and medical data science. His work often involves large-scale population studies and interdisciplinary collaborations across medical specialties.

• Child and Adolescent Psychology
• Clinical Psychology
• Obsessive-Compulsive Disorder
• +۳ مورد دیگر

Camilla Funch Uhre serves as an External Lecturer at the Department of Psychology, University of Copenhagen, where she participates in the Study Board of Psychology. Her institutional address is Øster Farimagsgade 2A, 1350 København K. Her research centers on child and adolescent clinical psychology, with specialized focus on obsessive-compulsive disorder (OCD) and cerebral palsy. She investigates cognitive functioning, therapeutic interventions including cognitive behavioral therapy, and biomarkers such as salivary oxytocin and vocal features. Her work emphasizes protocol development for clinical trials and interdisciplinary collaboration in pediatric neuropsychology. Analysis of her 2022-2024 publications reveals consistent focus on OCD treatment methodologies for youth, particularly family-based cognitive behavioral therapy versus relaxation therapy (TECTO trial). Emerging trends include machine learning applications for vocal feature analysis in OCD severity assessment and exploration of neuroendocrine biomarkers during puberty. Her research bridges clinical psychology, developmental neuroscience, and digital phenotyping to advance evidence-based pediatric mental health interventions.

• Computational Biology
• Structural Biology
• Molecular Dynamics
• +۴ مورد دیگر

Emil Thomasen serves as a Guest Researcher in the Department of Biology within the Biomolecular Sciences unit at the University of Copenhagen's Faculty of Science. His research integrates computational methods with experimental structural biology approaches to investigate complex biomolecular systems. Position: Guest Researcher Institution: University of Copenhagen Department: Department of Biology Research Unit: Biomolecular Sciences Contact: fe.thomasen@bio.ku.dk | +4535323586 Thomasen's research focuses on developing and applying computational methodologies to understand protein structure, dynamics, and interactions. His work bridges quantum mechanics, molecular dynamics simulations, and experimental techniques including small-angle X-ray scattering (SAXS) and cryo-electron microscopy. He has made significant contributions to coarse-grained modeling, particularly with the Martini force field, and has pioneered approaches combining machine learning with quantum mechanical calculations for large molecular assemblies. His publication record demonstrates a clear trajectory in computational structural biology, with recent work emphasizing the integration of multiple experimental and computational techniques to characterize complex protein systems. The research shows increasing sophistication in handling large-scale biomolecular assemblies and applying advanced statistical methods like Bayesian inference for model refinement. Thomasen has established strong collaborative networks, frequently working with Professor K. Lindorff-Larsen and other researchers across computational chemistry and structural biology disciplines. His work has appeared in high-impact journals including Nature Communications, eLife, and Journal of Chemical Theory and Computation. As a recently completed Ph.D. graduate (2024), Thomasen continues to advance his research program as a Guest Researcher, building on his doctoral work focused on integrating coarse-grained molecular dynamics with SAXS data for characterizing multidomain protein assemblies.

Helene Charlotte Wiese Rytgaardمشاهده پروفایل

دانشیار

• Biostatistics
• Causal Inference
• Targeted Machine Learning
• +۴ مورد دیگر

Helene Charlotte Wiese Rytgaard serves as an Associate Professor in the Department of Public Health, Section of Biostatistics at the University of Copenhagen's Faculty of Health and Medical Sciences. Her research focuses on methodological advancements in causal inference, targeted machine learning, event history analysis, and nonparametric estimation. Her primary research interests center on developing statistical machine learning methods for estimating intervention effects in time-varying settings, with specific applications in medical and epidemiological studies. Key areas include survival analysis, competing risks, target population treatment effects, and cumulative incidence estimation. Her work bridges theoretical statistics with practical applications in public health research. Recent publications demonstrate consistent contributions to biostatistical methodology, particularly in targeted maximum likelihood estimation and causal inference frameworks. Her research output shows strong collaboration patterns across epidemiology, psychiatry, and cardiovascular medicine, with significant emphasis on real-world data applications and methodological innovations for complex time-to-event data. Rytgaard maintains active research collaborations across international networks, as evidenced by her extensive co-authorship patterns in high-impact journals. Her work frequently addresses challenges in medical research design and analysis, particularly in observational studies and clinical trials requiring sophisticated statistical approaches.

Michael Charles Sachsمشاهده پروفایل

دانشیار

• Biostatistics
• Causal Inference
• Survival Analysis
• +۳ مورد دیگر

Michael Charles Sachs serves as Associate Professor in the Department of Public Health, Section of Biostatistics at the University of Copenhagen's Faculty of Health and Medical Sciences. He holds a Doctorate in Biostatistics from the University of Washington (awarded August 15, 2011) and maintains an active research program with publications spanning 2022-2025. His primary research focuses on biomarker signatures for treatment selection , computational methods for deriving symbolic bounds on causal effects , and regression modeling of cumulative estimands with censored event history data . His general areas of interest include reproducible research, computational statistics, R programming, Bayesian nonparametric models, and survival analysis. Professor Sachs teaches the advanced course "Programming and Statistical Modeling in R" for PhD students and health researchers, covering programming principles, loops, functions, and efficient data manipulation. His recent publications demonstrate expertise in causal inference methodology, survival analysis techniques, and statistical software implementation, with multiple articles accepted for publication in 2025. His work spans theoretical methodology development and practical applications in medical research, particularly in gastroenterology and celiac disease prediction. His scholarly output shows consistent productivity with multiple first-author publications in high-impact biostatistics journals including Journal of Statistical Software, Journal of Computational and Graphical Statistics, and Biostatistics. He actively organizes academic activities, including a May 2025 workshop on statistical modeling in R.

• Computational Biology
• Metagenomics
• Bioinformatics
• +۵ مورد دیگر

Jakob Nybo Nissen serves as an Assistant Professor and Affiliate Professor with the Rasmussen Group at the Faculty of Health and Medical Sciences, University of Copenhagen. His research focuses on computational biology with emphasis on metagenomics, bioinformatics, and machine learning applications for genomic data analysis. Dr. Nissen's primary research interests include: Metagenome binning and taxonomic classification Application of deep learning to genomic sequence analysis Development of computational tools for microbial community analysis Influenza virus evolution and zoonotic transmission Multi-omics data integration for disease research His recent publications demonstrate a strong focus on improving computational methods for metagenomic analysis, with several papers published in high-impact journals including Nature Communications, Nature Biotechnology, and Nature Methods. His work bridges computer science and biology, developing novel algorithms that address critical challenges in genomic data processing. Dr. Nissen is actively involved in collaborative research efforts, particularly through the Rasmussen Group, and has contributed to major initiatives like the Critical Assessment of Metagenome Interpretation. His work has practical applications in microbial ecology, disease surveillance, and personalized medicine approaches. Based at the Center for Protein Research at the University of Copenhagen, Dr. Nissen works in a vibrant research environment that integrates computational and experimental approaches to address fundamental biological questions with biomedical relevance.

Dhouha Grissaمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Dhouha Grissa serves as an Assistant Professor in the Department of Veterinary and Animal Sciences at the University of Copenhagen, Denmark, specializing in Preclinical Disease Biology. Her research applies advanced data mining and knowledge extraction techniques to biomedical challenges, with primary focus on early detection of alcoholic liver fibrosis through analysis of medical records and metabolomic data. Education: PhD in Computer Science, University of Clermont-Auvergne, France (awarded November 2, 2013) Research Interests: Dr. Grissa's work integrates bioinformatics, machine learning, and computational biology to develop knowledge discovery frameworks for metabolomic and clinical data. She specializes in identifying predictive biomarkers for disease progression, particularly in alcoholic liver disease, through hybrid analytical approaches combining text mining, process mining, and multi-omics integration. Publication Trends: Her recent publications (2020-2022) reveal a consistent trajectory in applying data integration and knowledge extraction to biomedical databases and clinical registries. Key themes include disease-gene association mapping, GWAS analytics for drug target illumination, and exploratory metabolomic analysis—all converging on improving disease prediction models and understanding pathophysiological mechanisms. Advising and Grants: No specific information regarding student advising or grant funding was provided in the source material. Labs and Teams: Dr. Grissa is embedded within the Novo Nordisk Foundation Center for Protein Research and actively contributes to the EU-funded Galaxy project, which develops integrated systems models for multi-omics and clinical data analysis using resources like the Danish National Patient Registry.

Michael Eriksen Benrosمشاهده پروفایل

استاد بالینی

• Immuno-Psychiatry
• Biological Psychiatry
• Epidemiology
• +۵ مورد دیگر

Michael Eriksen Benros is a Clinical Professor at the Department of Clinical Medicine, University of Copenhagen, and heads the Biological and Precision Psychiatry unit at Mental Health Centre Copenhagen, Copenhagen University Hospital. He serves as a board member of the Psychiatric Immunology Section of the World Psychiatric Association and is co-PI on the DanFunD study. Dr. Benros is a leading expert in Immuno-Psychiatry, Biological Psychiatry, Epidemiology, and Precision Psychiatry. His research focuses on the intersection of immune-related factors and mental illness, utilizing Danish nationwide registers, biobanks, and clinical studies. He leads significant efforts to understand the role of the immune system in severe mental disorders through immunogenetic investigations and novel research on cerebrospinal fluid and blood samples from patients with psychotic and affective disorders. His most recent publications (2025) demonstrate a continued focus on neuroimmunology, with studies examining the neurological impacts of COVID-19, perinatal immune markers and ADHD, genetic prediction methods, and lifestyle factors in functional somatic disorders. Earlier landmark publications (2020) established foundational work in autoimmune psychosis and the mental health consequences of the pandemic. His scientific recognition includes: Sapere Aude Research Leader award from the Independent Research Fond Denmark Dr. Benros has secured substantial research funding for his work on immuno-psychiatry and precision psychiatry, including leadership of the Precision Psychiatry Initiative which utilizes systems biology and machine learning methods to develop more precise diagnostics and identify new treatment targets. His research leverages Denmark's unique nationwide registers and biobanks to investigate immune exposures in relation to mental disorders. He directs a significant research group focused on Biological and Precision Psychiatry at Mental Health Centre Copenhagen, where they combine register data with immunogenetic investigations and novel research on cerebrospinal fluid and blood from clinical studies. His team employs omics and systems biology approaches to unravel the complex relationship between the immune system and mental health.

• Rheumatology
• Autoimmune Diseases
• Musculoskeletal Imaging
• +۲ مورد دیگر

Mikkel Østergaard is a Clinical Professor at the Department of Clinical Medicine within the Faculty of Health and Medical Sciences at the University of Copenhagen. Based at hospital campuses in Glostrup and Copenhagen, Denmark, he maintains an active dual clinical-academic role through the Capital Region of Denmark (Region Hovedstaden). His research concentrates on rheumatology with emphases on: Autoimmune disease mechanisms (particularly rheumatoid arthritis and spondyloarthritis) Advanced imaging techniques including musculoskeletal ultrasound and MRI Metabolomics and biomarker discovery for treatment response Artificial intelligence applications in rheumatic disease diagnostics Medical education methodologies for procedural skills assessment Professor Østergaard's extensive publication record (826 outputs) demonstrates significant international collaboration across European rheumatology networks. His 2025 research outputs reveal a strong focus on translational projects bridging laboratory findings with clinical applications, particularly in metabolic fingerprinting of synovial tissues, immune checkpoint dynamics, and AI-driven diagnostic tools. He frequently contributes to multi-center clinical trials and European registry initiatives. His work in medical education includes the development of validated assessment frameworks for ultrasound-guided interventions, reflecting commitment to training next-generation rheumatologists. Current projects leverage large-scale datasets from phase 3 trials and pan-European collaborations to address methodological challenges in observational rheumatology research.

• Cancer Genomics
• Genome Instability
• DNA Repair
• +۷ مورد دیگر

Fran Supek is a Professor at the University of Copenhagen's Faculty of Health and Medical Sciences, where he leads the Genome Data Science laboratory at the Biotech Research & Innovation Centre (BRIC). He also maintains a research group at the IRB Barcelona. Supek is the Principal Investigator of ERC Starting and ERC Consolidator projects, an EMBO Young Investigator, and holds an ICREA tenured professor position (currently on leave). Dr. Supek obtained his PhD in Molecular Biology in 2010 from the University of Zagreb, followed by postdoctoral training at the Centre for Genomic Regulation (CRG) as a Marie Curie fellow. He established his independent laboratory at IRB Barcelona in 2017 as a "Ramón y Cajal" tenure-track fellow, and expanded to BRIC in 2024. His research focuses on statistical genomics and machine learning approaches to study genome stability mechanisms, particularly through analysis of mutation patterns in cancer. The Genome Data Science lab performs large-scale bioinformatic studies of multi-omic data from human tumors, populations, and metagenomes. Key research areas include DNA repair mechanisms, mRNA synthesis and turnover pathways, genetic interactions in cancer genomes, and machine learning approaches for inferring gene function from genomic data. Analysis of Supek's recent publications reveals a strong emphasis on cancer genomics, with particular focus on mutational signatures, DNA repair deficiencies, and the application of artificial intelligence to genomic data analysis. His work spans from fundamental mechanisms of genome instability to translational applications in cancer diagnosis and treatment. ERC Starting Grant recipient ERC Consolidator Grant recipient EMBO Young Investigator ICREA tenured professor (on leave) As Principal Investigator of multiple ERC projects, Supek leads a research program with significant funding for innovative genomic research. His laboratory at BRIC and IRB Barcelona conducts interdisciplinary research combining computational approaches with experimental validation to address fundamental questions in genome biology and cancer evolution. The Genome Data Science lab maintains active collaborations across multiple institutions, with research spanning statistical genomics, machine learning applications in bioinformatics, and experimental validation of computational predictions in cancer biology.

• Breast cancer screening
• Cervical cancer screening
• HPV vaccination
• +۵ مورد دیگر

George Napolitano is an Academic employee at the University of Copenhagen's Department of Public Health within the Section of Environmental Health. His work focuses on providing statistical support for various research projects in public health, with particular expertise in cancer screening programs and environmental health research. Dr. Napolitano holds a PhD in Mathematics from the Department of Mathematical Sciences and an MSc in Physics from the University of Naples Federico II. Prior to his current position, he worked as a Postdoc/Researcher at the Centre for Mathematical Sciences from 2013 to 2017, where he conducted research in Theoretical and Mathematical Physics and Applied Probability. His primary research interests include breast cancer screening, cervical cancer screening, HPV vaccination, and allostatic load. His expertise in statistical analysis bridges advanced mathematical methodologies with practical public health applications. Dr. Napolitano has published extensively, with 51 research outputs documented in his profile. His recent publications (2024-2025) demonstrate a strong focus on cervical cancer screening outcomes among HPV-vaccinated women through the Trial23 cohort study, breast cancer screening methodologies including tomosynthesis and deep learning approaches, and environmental health research examining air pollution's impact on lung cancer, respiratory infections, and COVID-19 mortality. His work consistently applies sophisticated statistical methods to address critical questions in epidemiology. Dr. Napolitano maintains an active research profile with multiple publications receiving significant attention from news outlets and academic platforms. His interdisciplinary background in theoretical physics and mathematics provides unique expertise in developing and applying complex statistical models to health data, making substantial contributions to cancer epidemiology and environmental health research.

• Actuarial Science
• Statistics
• Machine Learning
• +۲ مورد دیگر

Frederik Hytting Jørgensen serves as a Research Fellow at the Department of Mathematical Sciences, University of Copenhagen. His research spans critical domains in quantitative analysis: Actuarial Science with emphasis on fairness frameworks Statistical causal inference methodologies Machine learning ethics in insurance applications Risk modeling for insurability assessment Algorithmic bias mitigation techniques His 2024 publication in the European Actuarial Journal pioneers connections between plurality of perspectives, causal mechanisms, and insurability constraints, establishing foundational work for ethically robust actuarial models that balance technical precision with social responsibility in algorithmic decision-making systems.