جستجوی استاد راهنمای دانشگاهی

Michał Baranowskiمشاهده پروفایل

مدرس

• microwave filters
• design optimization
• electronic design automation
• +۳ مورد دیگر

Michał Baranowski serves as an Assistant lecturer at the Department of Microwave and Antenna Engineering within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology since 2021. He is simultaneously pursuing his PhD at the same institution, demonstrating a strong commitment to both teaching and research in microwave engineering. His research interests focus on advanced microwave component design, particularly in the areas of microwave filters, design optimization techniques, electronic design automation, finite element method applications, computational electromagnetics, and specialized microwave design methodologies. His work bridges theoretical concepts with practical manufacturing approaches, especially regarding additive manufacturing techniques for microwave components. Baranowski's publication record (13 papers from 2020-2025) reveals a clear research trajectory centered on innovative approaches to microwave filter design and waveguide component manufacturing. His most recent work (2024-2025) emphasizes 3D-printed microwave components, shape deformation techniques for resonator design, and methods to improve surface quality of additively manufactured waveguides. This research has direct applications in satellite communications, Earth observation systems, and 5G technology infrastructure. His technical expertise spans computational electromagnetics, optimization algorithms, and advanced manufacturing techniques for high-frequency components, with a particular emphasis on making these technologies more accessible through open platform tools and cost-effective manufacturing approaches. As an active researcher and educator, Baranowski contributes to both the theoretical advancement and practical implementation of microwave engineering solutions, with his work appearing in prestigious journals including IEEE Microwave and Wireless Technology Letters, IEEE Transactions on Microwave Theory and Techniques, and IEEE Access.

Marek Tataraمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Computer Vision
• +۵ مورد دیگر

Dr. Marek Tatara serves as an Assistant Professor at the Department of Decision Systems and Robotics within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic work spans multiple engineering disciplines with a focus on artificial intelligence applications across diverse domains. Dr. Tatara's research interests encompass Artificial Intelligence , Robotics , Computer Vision , and Music Technology , demonstrating a unique interdisciplinary approach. His work bridges theoretical computer science with practical engineering applications, particularly in developing systems that integrate machine learning with physical processes. His publication record shows consistent output with research spanning from 2022 to 2025, with recent work focusing on generative AI for music composition, vision systems for robotics, and cyber-physical systems. The publications demonstrate a pattern of collaborative research across international teams and appear in reputable journals including IEEE Transactions on Automation Science and Engineering. Dr. Tatara maintains active contact through his university email martatar@pg.edu.pl and phone number 583471668, indicating ongoing academic engagement at Gdańsk University of Technology.

Alicja Olejniczakمشاهده پروفایل

مدرس

• Deep Learning
• Wireless Communications
• LTE Technology
• +۶ مورد دیگر

mgr inż. Alicja Olejniczak serves as a Lecturer at the Department of Radiocommunication Systems and Networks within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. Her work focuses on the intersection of wireless communications and artificial intelligence, particularly applying deep learning techniques to solve challenging problems in modern radio systems. Her primary research interests include: Deep learning applications in wireless communication systems Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) identification in indoor environments Signal processing for GMSK modulation schemes Software-defined radio implementations for NB-IoT technologies Train detection using LTE reference signals Radiolocation systems and multilateration techniques Analysis of her recent publications reveals a strong trend toward applying deep learning methods to solve practical problems in wireless communications. Her work spans both theoretical development and practical implementation of algorithms for signal processing, channel estimation, and radio interface design. A significant portion of her research focuses on NB-IoT and LTE technologies, with applications ranging from Internet of Things to transportation systems like train detection. Dr. Olejniczak actively participates in multiple research projects at Gdańsk University of Technology, including: SDIDS - Software-Defined Device for Detecting and Mitigating Interferences in 4G-LTE and 5G-NR radio interfaces DUCH IoT - A software-defined, universal radio interface for intelligent IoT devices VCS-MLAT - Innovative method of aircraft locating in distributed VCS systems AEGIS - Mobile Device for Generating Electromagnetic Curtain Her laboratory work involves hands-on implementation of software-defined radio systems and conducting measurements in real-world environments to validate theoretical models. She collaborates extensively with colleagues including O. Błaszkiewicz, K. Cwalina, P. Rajchowski, and J. Sadowski on various research initiatives.

Jan Glinkoمشاهده پروفایل

مدرس

• Computer Vision
• Robotics
• Deep Learning
• +۷ مورد دیگر

Jan Glinko is an Assistant lecturer at the Department of Decision Systems and Robotics within the Faculty of Electronics Telecommunications and Informatics at Gdansk University of Technology. His academic work spans across multiple disciplines including robotics, computer vision, and molecular biology. His research interests focus on: Computer Vision and Deep Learning applications Robotics, particularly pick-and-place systems and autonomous manipulation Molecular dynamics and G-quadruplex topology analysis Synthetic dataset generation for deep learning model training Dr. Glinko's recent publications demonstrate a strong interdisciplinary approach, bridging the gap between robotics and molecular biology. His work on G-quadruplex topological landscape represents a significant contribution to understanding nucleic acid structures, while his robotics research focuses on practical applications of deep learning for autonomous systems. His scientific contributions include: Developing methods for training deep learning models using synthetic datasets Creating autonomous pick-and-place systems based on multiple 3D sensors Advancing the understanding of G-quadruplex topological landscape through large-scale conformational analysis At Gdansk University of Technology, Jan Glinko teaches various courses related to digital systems, programming fundamentals, and intelligent robotics. His teaching portfolio includes courses for Automatic Control, Cybernetics and Robotics programs, as well as supporting laboratory sessions for digital technology and logic systems.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Marcin Sawiński is a Researcher and Teaching Assistant at the Department of Information Systems within the Institute of Informatics and Quantitative Economics at Poznań University of Economics and Business. With expertise spanning computer and information sciences (75%) and management and quality studies (25%), his work focuses on developing AI tools for addressing misinformation and enhancing fact-checking processes. His primary research interests include: Artificial Intelligence applications for fake news detection Natural Language Processing techniques for misinformation analysis Machine learning approaches to credibility assessment Persuasion techniques detection in social media content Development of robust language models for fact-checking systems Analysis of political narratives during crisis events like pandemics Dr. Sawiński's recent publications (2023-2025) demonstrate a concentrated research trajectory focused on applying transformer-based models to misinformation challenges, particularly in Slavic languages. His work spans multiple dimensions of the fake news problem, from detection of persuasion techniques to cross-lingual transfer learning for check-worthiness assessment. He has been actively involved in international competitions like CheckThat! Lab at CLEF, where his team (OpenFact) has developed innovative approaches to fact-checking challenges, including adversarial text generation to test model robustness. His scientific contributions include 15 publications with over 50 citations, reflecting his growing impact in the field of AI for misinformation detection. His research often involves collaboration with colleagues including Krzysztof Węcel, Witold Abramowicz, and Ewelina Księżniak.

Andrzej Gnatowskiمشاهده پروفایل

پژوهشگر

• Operational research
• Parallel programming
• Robust optimization
• +۱ مورد دیگر

Dr. Eng. Andrzej Gnatowski is a faculty member at the Wrocław University of Science and Technology , affiliated with the Department of Computer Science and Engineering. His research interests span operational research, parallel programming, robust optimization, and neural network-based image analysis. Operational Research Parallel Programming Robust Optimization Image Analysis using Neural Networks His work bridges theoretical optimization techniques with practical applications in computational methods and machine learning, emphasizing scalable solutions for complex systems.

Edward Kozłowskiمشاهده پروفایل

استاد

• Stochastic Control Systems
• Optimization
• Decision Support Systems
• +۳ مورد دیگر

Dr. Edward Kozłowski is a Professor at the Department of Quantitative Methods in Management within the Faculty of Management at Lublin University of Technology. He holds consultations every Wednesday from 10:00 to 12:00 in room 20 of the New Oxford Connecting Building and can be reached via email at e.kozlovski@pollub.pl or through his personal website. Dr. Kozłowski earned his academic credentials through a rigorous path: completing his Master's degree in 1996 from the Faculty of Mathematics and Physics at Maria Curie-Skłodowska University, his PhD in 2002 at the Systems Research Institute of the Polish Academy of Sciences in Warsaw with a thesis on 'Optimization of investment portfolio taking into account the cost of information,' and achieving habilitation in 2019 from the same institution. His research focuses on optimal adaptive control for stochastic systems with random horizon and modeling decision support and control systems under uncertainty. This work spans stochastic optimization, machine learning applications, statistical modeling, and decision theory under uncertain conditions. Dr. Kozłowski's approach consistently bridges theoretical mathematical frameworks with practical applications in management contexts. Analysis of his recent publications (2017-2020) reveals a strong trend toward interdisciplinary applications of quantitative methods. His work spans pandemic response modeling, manufacturing optimization, water resource management, transportation systems analysis, and building diagnostics. The integration of machine learning techniques with traditional statistical approaches has become increasingly prominent in his recent research. Dr. Kozłowski teaches courses in forecasting and simulations, descriptive statistics, and mathematical statistics, emphasizing practical applications of quantitative methods in management. His teaching philosophy aligns with the Department of Quantitative Methods in Management's postgraduate studies in Data Analysis, which focuses on teaching statistical methods as practical tools rather than theoretical constructs. His collaborative research network includes frequent partnerships with Tomasz Rymarczyk, Grzegorz Kłosowski, Anna Borucka, and Dariusz Mazurkiewicz across diverse application domains. This collaborative approach has resulted in numerous publications in reputable journals and conference proceedings.

• Applied mathematics
• Mathematical modeling
• Data analysis
• +۲ مورد دیگر

Konrad Kania is a Lecturer at the Department of Quantitative Methods in Management within the Faculty of Management at Lublin University of Technology. He has been working as a researcher and lecturer since 2019, initially at the Faculty of Management at the Faculty of Electrical Engineering and Computer Science, and since 2020 at his current department. His research focuses on applied mathematics, mathematical modeling, data analysis, machine learning, and deep learning, with particular emphasis on tomography applications. Kania's work bridges theoretical mathematical approaches with practical industrial implementations, especially in ultrasound and electrical tomography systems. Analysis of his publications from 2019-2021 reveals a strong focus on developing innovative algorithms for image reconstruction in various tomographic systems. His research spans multiple disciplines including computer science, electrical engineering, physics, and industrial applications, with particular emphasis on real-time processing, anomaly detection, and machine learning applications in tomography. Kania teaches statistical methods, optimization methods, mathematics, and seminars on Using R in Quantitative Methods, demonstrating his commitment to both theoretical and applied aspects of quantitative methods in management education.

• Artificial intelligence
• Manufacturing
• Metrology
• +۴ مورد دیگر

Daniele Scrimieri serves as Assistant Professor in Computer Science at the University of Bradford's Faculty of Engineering & Digital Technologies, where he leads the BSc Computer Science for Cyber Security and MSc Cyber Security programmes. Education: PhD from the University of Bari 'Aldo Moro' (Italy) Research Profile: Dr. Scrimieri's work bridges artificial intelligence with manufacturing systems, focusing on automated process planning, control mechanisms, and quality inspection technologies. His recent research has expanded into cyber security applications and healthcare machine learning, reflecting his interdisciplinary approach that combines theoretical frameworks with practical industrial solutions developed through extensive industry collaboration. Professional Recognition: Member of EPSRC Peer Review College Editorial board member of Journal of Universal Computer Science Peer reviewer for Engineering Applications of Artificial Intelligence, Journal of Intelligent Manufacturing, and Additive Manufacturing Dr. Scrimieri has secured significant research funding as Co-Investigator for the Innovate UK 'Smart and Sustainable Manufacturing for Baking Industry' project and as Principal Investigator for Bradford's SURE Research Projects Fund initiative on manufacturing process controllers. His industry background includes 8 years at LK Metrology, Nikon Metrology and MERMEC developing metrology software for major clients including BAE Systems, Volvo, and Safran Aircraft Engines. He previously contributed to European smart factory projects (EC FP7 FRAME, PRIME, Copernico) at the University of Nottingham's Institute for Advanced Manufacturing. As an educator, Dr. Scrimieri teaches Software Systems Design and Testing, Cyber Physical Systems Security, and Internet of Things. Both his undergraduate and postgraduate Cyber Security programmes hold provisional accreditation from the National Cyber Security Centre. He serves on the Faculty Research and Innovation Committee, Postgraduate Research Sub-Committee, and acts as external examiner for two UK universities.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۲ مورد دیگر

Lecturer Łukasz Dzierżanowski is affiliated with the Department of Artificial Intelligence at Opole University of Technology, where he contributes to academic instruction and international collaboration initiatives. His research expertise spans Artificial Intelligence, Machine Learning, and Data Science, with emphasis on developing computational models and intelligent systems applicable to real-world engineering challenges. This focus aligns with the department's specialization in cutting-edge AI methodologies and algorithmic innovation. He serves as the Erasmus+ Faculty Coordinator, actively managing international academic exchange programs to foster global partnerships and student mobility opportunities.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Paweł Michalski serves as a Lecturer at the Department of Artificial Intelligence, Opole University of Technology, actively engaged in teaching and academic duties within the institution's current faculty structure. His research spans foundational Artificial Intelligence disciplines with specific emphasis on Machine Learning methodologies, Data Science applications, Computer Vision systems, Natural Language Processing techniques, and Deep Learning architectures, reflecting the department's core technological focus areas. Consultation sessions for students are scheduled every Saturday from 12:40 to 14:30 in room P1-213. Primary contact channels include telephone 77 449 8033 and institutional email p.michalski@po.edu.pl, with additional resources accessible through the PO Knowledge Base and employee portal.