After completing his vocational training as farmer, Michael Iwersen studied agricultural science with a focus on animal production at the Christian-Albrechts University Kiel, Germany. During his studies, he worked on a commercial dairy farm and after graduating he was fully responsible for the herd management of approx. 1,000 animals, supervising 30 employees and several trainees. In 2000, he started studying veterinary medicine, which he completed in 2006 at the Freie Universitaet Berlin, Germany. Michael joined the team of the Clinical Unit for Herd Health Management at the University Clinic for Ruminants, University of Veterinary Medicine Vienna (Austria) as Senior Postdoc in 2010. Since the beginning of his career, livestock reproduction and herd health management were the focus of his research activities. As data analysis, modern sensor technology and digital solutions are becoming more and more an integrated part of herd management, Michael and his group initiated several research projects and cooperations in this field, with numerous publications and theses as scientific output. Within the context of „Precision Livestock Farming” (PLF), Michael and his group are partners in funded projects and consortia, such as FFG-funded projects AgriProKnow, D4Dairy, FFoQSI, Industrienahe Dissertationen, and “Digitalisierungs- und Innovationslabor in den Agrarwissenschaften” (DiLaAg), funded by Stiftung Forum Morgen and Land Niederösterreich, as well as industry funded projects. Michael’s current activities in the frame of this „PLFDoc” application are focused on the sensor-based monitoring of cattle. His applied research has the aim to evaluate the potentials and benefits of sensor-based monitoring systems with regard to animal health and welfare as well as the social-economic effects of these technologies on farmers, veterinarians and relevant stakeholders. Since 2023, Michael Iwersen is Head of the doctoral school PLFDoc.

Daniela Klein-Jöbstl is a veterinarian working as a researcher and clinician at the Clinical Unit for Herd Health Management in Ruminants at the University of Veterinary Medicine Vienna. Her research is focused on different topics of herd health management in cattle, especially in calves and youngstock. The research works includes microbiology, use of (electronic) devices for on farm testing, and Precision Livestock Farming. She is an author of 36 peer-reviewed publications and 45 conference abstracts. She co-supervised six doctoral students. Currently she supervises one doctoral student in a project on PLF in calves.

Maciej Oczak is a coordinator of PLF HUB, which is currently a research group affiliated to the Institute of Animal Welfare Science. His role as the HUB coordinator is to facilitate research at Vetmeduni related to animal monitoring with sensor technologies and to bridge the gap between engineering and animal science at the university. Maciej Oczak is a bioscience engineer with over seven years of experience in the field of PLF, which he obtained in The Netherlands, Belgium and Austria. He has four years of experience working for companies leading in the application of PLF in practical on-farm conditions (Fancom, The Netherlands and Smartbow, Austria). Maciej Oczak finished his PhD at M3 BIORES research group at Catholic University in Leuven, Belgium. Maciej PhD dissertation “Precision Livestock Farming for sows and weaner pigs” focused on monitoring of aggression in weaner pigs (EU-BioBusiness) and on monitoring sows’ behavior in the period before farrowing and piglet behavior during farrowing. Maciej Oczak is an author of 30 peer-reviewed publications. Maciej Oczak currently co-supervises 2 PhD theses.

Stephan Winkler studied computer science at Johannes Kepler University (JKU) and is member of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL, led by Prof. Michael Affenzeller) since 2002. In 2004 he joined the Institute for Design and Control of Mechatronical Systems at JKU as research assistant. 2006 he became research assistant at University of Applied Sciences, Hagenberg (FH Upper Austria) and worked on his PhD thesis “Evolutionary System Identification - Modern Concepts and Practical Applications'' (JKU, Institute for Formal Models and Verification, 2008). In 2009 Dr. Winkler was appointed professor at the Department of Medical and Bioinformatics at FH Upper Austria. Since 2011, he serves as head of the Bioinformatics Research Group. In 2018 he received his habilitation from the Johannes Kepler University Linz; since then, he has served as supervisor of several PhD students in bioinformatics and computer science.

The research interests of Stephan Winkler include ML, especially white box modeling based on genetic programming, bioinformatics, image analysis, and nonlinear model identification. Stephan Winkler actively collaborates with numerous national and international research institutions and companies, amongst others he served as project leader of research projects sponsored by the FFG. He was also acting as key researcher in several FFG-sponsored projects, an EFRE-project, and as co-project-leader of the FWF project SESAM; furthermore, he is member of the TIMED scientific board. Stephan Winkler is (co-)author of more than 50 peer-reviewed journal publications.

Since 2022, Stephan Winkler is Scientific Head of Softwarepark Hagenberg, where he coordinates the scientific activities of companies and research institutions in Hagenberg (total: ca. 3000 people).

Viktoria Dorfer is Professor for Bioinformatics at FH Upper Austria at the department “Medical- and Bioinformatics”, where she teaches proteomics as well as algorithm development and programming. Viktoria Dorfer studied Bioinformatics at the University of Applied Sciences Upper Austria and received her PhD in informatics from the Johannes Kepler University Linz. Her research interests focus on computational proteomics and ML, especially on peptide identification, which was also the topic of her PhD thesis, entitled “Identification of Peptides and Proteins in High-resolution Tandem Mass Spectrometry Data”. Part of her doctoral research was the development of the peptide identification algorithm MS Amanda.

Dr. Dorfer is member of the Bioinformatics Research Group at FH OÖ since its foundation in 2011, where she leads the Computational Proteomics subunit. The main focus of this subunit is the development of algorithms and software for the identification of peptides and proteins in mass spectrometry data. As researcher, project leader and collaboration partner, Dr. Dorfer has published a plethora of peer-reviewed publications in high impact proteomics journals, where Dr. Dorfer is either first, last, or co-author.

Amongst other successes, two major proteomics projects headed by Dr. Dorfer were recently granted:

• FWF funded research project “Neue Tools für Proteomweite Crosslinking Massenspektrometrie”, where Dr. Dorfer acts as national research partner
• EU Horizon 2020 research and innovation programme funded Marie Skłodowska-Curie grant “PROTrEIN”, in which Dr. Dorfer acts as one of the beneficiaries

In that respect, she is currently the technical PhD supervisor of 3 PhD students and has also assessed several external PhD theses over the last years. In the last years, Dr. Dorfer was head of several other third party funded research projects with various companies, e.g., “Screening 2.0”, an FFG funded project with several partners from industry and research, or “b-tastic”, an FFG funded basic research project with b-tastic Sports GmbH.

Dr. Dorfer is a founding member of EuBIC-MS, the European Bioinformatics Community for Mass Spectrometry, a community aiming to improve bioinformatics in computational omics research. In that respect, Dr. Dorfer actively organized or co-organized several proteomics bioinformatics events, including the EuBIC Winter School 2017 and 2019, the EuBIC Developers’ meeting 2018 and 2020, the Lorentz Center Workshop on Proteomics and Machine Learning 2022, and is currently organizing the next EuBIC Winter School 2024. She also received the EuPA Bioinformatics for Mass Spectrometry Award in 2020.

A Min Tjoa is an Emeritus Professor for Information and Software Engineering at Vienna University of Technology. Since 2008 A Min Tjoa is Delegate of the United Nations Committee on Science and Technology for Development (UN-CSTD) being its Chairperson in 2019 and its Vicechair from 2020-2022. A Min Tjoa’s  research activities in the scope of this application are focused on data integration and the ontology based utilisation of linked (open) data. The integration of (open) data sets raises a number of challenges for research in the area of terminology. Innovative (Linked) Open Data integration concepts deliver new opportunities for analytics of data from various sources.

The real-time recording and storage of large amounts of biological data, which are enriched by movement data, form the basis for statistical and machine-learning based analyses. An improved data extraction and its possible enhancement with available sources from different disciplines of this project will constitute the desired input for models leading to an automated real-time process control, which also includes herd health management.

The support in offering both the quality integration of data produced across the different involved disciplines and its design of processes that are nurtured by these data will  be one the main contributions of the institute in this project where ontology-based solutions are envisioned.

Margrit Gelautz is an associate professor at the Computer Vision Lab of the Institute of Visual Computing and Human-Centered Technology at TU Wien. She has performed research in the field of CV with a focus on 3D scene reconstruction (especially, stereo analysis), video object segmentation, image matting, and motion estimation. In the context of assisted/autonomous driving applications, her recent work has addressed the transfer of CV and machine learning techniques to low-visibility (nighttime) traffic scenes and associated techniques for semi-automatic ground truth annotation.

Ongoing research activities involve the tracking of objects and humans as well as pose estimation and motion transfer for human-robot interaction. These research topics share a variety of similarities and challenges with CV algorithms in farming environments, such as variable lighting conditions or low visibility, the treatment of occlusions, the development of strategies for domain adaptation and transfer learning, and the generation of ground truth data for training deep learning algorithms. Margrit Gelautz co-supervised 14 PhD students and is an author of 24 peer-reviewed journal publications