Find Academic Supervisors

• Data Science
• Econometrics
• Economics
• +5 more

Univ.-Prof. Martin Wagner is a Professor at the Department of Economics, Alpen-Adria-Universität Klagenfurt, where he serves as Vice-Dean of the Faculty of Management, Economics and Law and Head of the Quantitative Economics Division. His research bridges theoretical and applied econometrics with environmental and macroeconomic challenges. Affiliation: Department of Economics, Faculty of Management, Economics and Law, Alpen-Adria-Universität Klagenfurt Wagner’s work focuses on econometric theory and its applications, particularly in modeling nonlinear cointegrating relationships, environmental economics, and macroeconomic forecasting. His expertise includes: Cointegrating polynomial regressions Unit root processes and stationarity High-dimensional time series analysis Environmental Kuznets curves Panel data econometrics Macroeconomic modeling Recent publications highlight his contributions to robust estimation techniques and the intersection of climate science with economic growth dynamics. Contact: martin.wagner@aau.at

• Bioinformatics
• Computational Biology
• Phylogenetics
• +3 more

Prof. Manuela Schmidt is a faculty member at the University of Vienna, affiliated with the Faculty of Life Sciences and the Department of Pharmaceutical Sciences. Her research focuses on modern systems biology pain research and biomedical applications, primarily conducted in her laboratory. She supervises Master's theses with limited availability, requiring applications between January 1–31, 2025, and coordinates both experimental and literature-based thesis opportunities. Her work spans bioinformatics, computational biology, and phylogenetics, with significant contributions to maximum likelihood phylogenetic methods and software development (e.g., IQ-TREE). Research trends include evolutionary biology, molecular biology, and applications of machine learning in phylogenetic analysis. Additional interests involve environmental microbiology and developmental genetics. For contact, email manuela_schmidt@univie.ac.at. Her lab is located at UZA II, Josef-Holaubek-Platz 2, A-1090 Vienna, Austria (phone: +43-1-4277-55301).

• Plasma Physics
• Magnetic Confinement Fusion
• Computational Physics
• +4 more

Albert Christopher is an Associate Professor at the Institute of Theoretical Physics - Computational Physics, Technische Universität Graz. His research focuses on plasma physics, magnetic confinement fusion, and computational modeling of fusion devices such as tokamaks and stellarators. He specializes in symplectic integration techniques, neoclassical transport, and the development of hybrid kinetic-MHD models. Key projects include contributions to the EUROfusion program, analysis of resonant magnetic perturbations, and the design of 3D coil systems for future fusion reactors like EU-DEMO. He holds teaching authorizations in Theoretical Physics and Computational Physics. His work emphasizes plasma stability, edge transport modeling, and Bayesian inference for equilibrium reconstruction in devices like Wendelstein 7-X. Recent research includes studies on bootstrap current convergence in stellarators and the validation of numerical solvers for partially ionized plasmas. Christopher's publications span over a decade, addressing topics such as orbit classification in Hamiltonian systems, plasma response to magnetic perturbations, and uncertainty quantification in MHD simulations. His interdisciplinary approach integrates computational methods with experimental data from facilities like ASDEX Upgrade and TCV.

• Econometrics
• Time Series Analysis
• Statistical Learning
• +6 more

Karsten Reichold is an Assistant Professor of Econometrics at TU Wien (Vienna University of Technology), affiliated with the Institute of Statistics and Mathematical Methods in Economics within the Faculty of Mathematics and Geoinformation. His work bridges theoretical and applied econometrics with a focus on time series modeling and inference. His research interests include: Econometrics Time Series Analysis Statistical Learning Empirical Macroeconomics Forecasting Bootstrap Inference Cointegration Analysis His recent publications and software contributions reflect a strong focus on robust inference in cointegrating regressions using self-normalized test statistics and bootstrap methods. His work enables more reliable hypothesis testing in non-stationary economic time series data. Notable scientific contributions include: Development of self-normalized bootstrap inference for cointegrating regressions Implementation of Group-Mean Fully Modified OLS for panel cointegrating polynomial regressions He has advised no publicly listed students and has not received any explicitly mentioned awards. However, his publication in the Journal of Business & Economic Statistics indicates recognition in the field. He actively supports open science by providing ready-to-use MATLAB code on GitHub for reproducible research. He is involved in research projects related to: Bootstrap methodology in econometrics Panel cointegration Stochastic process modeling

• Plasma Physics
• Computational Material Science
• Quantum Many-Body Physics
• +3 more

Christopher Albert is an Associate Professor at Graz University of Technology, specializing in plasma physics and computational material science. His research focuses on magnetic confinement fusion, particularly in stellarators and tokamaks, with emphasis on guiding-center orbits, neoclassical transport, and MHD equilibrium modeling. He leads experimental initiatives like ALPES (Adaptable Lab for Plasma Experiments in Stellarators) and contributes to international fusion projects including EUROfusion and Wendelstein 7-X. Research Themes: Plasma dynamics, symplectic integration methods, magnetic perturbation effects, and fusion reactor diagnostics. Key Projects: ALPES lab development, EU-DEMO 3D coil optimization, and kinetic-MHD coupling models. Publication Trends: Recent work spans stellarator transport modeling, uncertainty quantification for MHD reconstructions, and resonant magnetic perturbation analysis across 15 articles (2023-2025).

• Robotics
• Artificial Intelligence
• Cognitive Science
• +4 more

Vishwanathan Mohan is a Lecturer (US equivalent: Assistant Professor) in Robotics and Artificial Intelligence at the University of Essex, UK. He was formally trained in Microelectronics and VLSI design at the Indian Institute of Technology Madras, and his scientific career began with neuromorphic VLSI models of associative memories. Education: Indian Institute of Technology Madras (Microelectronics and VLSI design) His research focuses on human-robot symbiosis, cognitive robotics, and embodied language learning. Key themes include goal-directed reasoning, cooperative robotics in shared workspaces, and neural frameworks for memory and body schema organization in robots. His work spans applications in industrial and humanoid robotics. Recent publications highlight neural models for social cognition, physical reasoning, and episodic memory systems. Collaborators include notable researchers such as Giulio Sandini and Pietro Morasso from the Italian Institute of Technology. As a Guest Associate Editor for Frontiers in Neurorobotics, he contributes to editorial roles. No scientific awards or advising details were explicitly mentioned in the available text.

• Econometrics
• Statistical Theory
• High-Dimensional Testing
• +4 more

Univ.Prof. David Preinerstorfer is a Professor at the Department of Statistics and Mathematics at WU Vienna University of Economics and Business. His research focuses on econometric theory, statistical methodology, and high-dimensional testing. He has published extensively in top journals such as Econometrica , Journal of Econometrics , and Annals of Statistics . Key research areas include heteroskedasticity and autocorrelation robust testing, high-dimensional statistical inference, bootstrap methods, and optimal policy learning. His work addresses challenges in econometric testing under complex data conditions and develops methods to enhance statistical power and validity. Recent publications analyze topics like modern Gauss-Markov theorems, superconsistency in high dimensions, and treatment allocation with distributional targets. Preinerstorfer collaborates frequently with researchers such as A. B. Kock and B. M. Pötscher on projects addressing robust testing procedures and statistical methodology advancements.

Andreas KurzView profile

Researcher

• Psychological Diagnostics
• Psychometrics
• Statistical Inference
• +6 more

Andreas Kurz is a Senior Scientist at the Department of Psychology, University of Salzburg, where he conducts research in psychological diagnostics and psychometric modeling. His work focuses on statistical inference in small sample contexts and conditional likelihood methods, particularly in educational assessment and admission procedures. Position: Senior Scientist Institution: University of Salzburg Department: Department of Psychology Research Group: Admission Procedure for Teaching Professions in the Central Cluster Andreas Kurz holds a Master of Science in Psychology from UMIT TIROL (2021) and a Diploma in Civil Engineering (equivalent to MSc ETH) from ETH Zurich (1995). His academic journey includes roles as a Student Assistant at UMIT TIROL (2019–2022) and a Scientific Assistant at ETH Zurich (2007–2009). His research lies at the intersection of psychology and statistics, with a strong emphasis on methodological rigor in psychometric testing. He develops and applies advanced statistical techniques for small sample inference, particularly using resampling and conditional likelihood frameworks. His work supports valid and reliable assessment in educational and psychological settings. The publications and software tools developed by Andreas Kurz reflect a consistent trend in advancing statistical methodology for psychometrics. His contributions include peer-reviewed articles on the gradient test and conditional inference, as well as R packages like tcl and tclboot that implement these methods for practical use in research and assessment. Andreas Kurz has actively participated in international conferences such as IMPS 2021 and IMPS 2022, presenting on statistical testing in conditional likelihood frameworks. His work is supported by research in psychometrics and statistical computing, with no explicit mention of external grants or advising roles. He is involved in the development of admission procedures for teaching professions, indicating applied research with societal impact in education. His technical expertise bridges civil engineering, psychology, and statistical programming, contributing to robust assessment systems.