Noldus Information Technology
Giorgos Dimopoulos is a Frontend Developer at Noldus Information Technology since September 2021, with previous experience as a Junior Geo-Information Researcher at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente. In this role, Giorgos utilized machine learning algorithms to develop forecast models and contributed to the creation of a GIS-based backend in Python. Prior to that, Giorgos was a Data Science Intern at Deltares, where the processing of satellite data and the design of an interactive Web GIS application were key responsibilities. Additional experience includes a project at KCAF focusing on monitoring subsidence of buildings using advanced sensors and a land surveying internship at the National Technical University of Athens. Educational qualifications include a Master's degree in Geomatics from Delft University of Technology and a Five Year Curriculum in Rural and Surveying Engineering from the National Technical University of Athens.
Noldus Information Technology
Noldus Information Technology develops, markets, and supports innovative software, instruments, integrated systems, and services for behavioral research. These allow scientists and practitioners to enhance the quality of their data, to increase productivity, and to make optimal use of human or animal resources. Our solutions are designed to meet the needs of our customers, offering them the following key benefits: - Enhanced quality: Our advanced technology allows more accurate measurement of behavior, and therefore a higher quality of collected data. - Increased productivity: Automated systems replace manual work and allow people to do more in less time. - Optimal use of human resources: By letting computers do the tedious routine work, researchers and technicians can focus on more interesting tasks. - Optimal use of animal resources: Our technology allows behavioral measurements to take place under natural and animal-friendly circumstances, while continuous automated observation maximizes the amount of information collected per animal. These advances contribute to the refinement and reduction of laboratory animal testing.