To make a good start into 2019 even better, how about state-of-the-art continuing education? The Deutsche Gesellschaft für Materialkunde (DGM) provides the opportunity to hear renowned researchers and practitioners from the field of material analytics and artificial intelligence (AI) in Dresden between May, 20th and 21st.
Lecturers are from: Fraunhofer IKTS, IWS, IPMS, GLOBALFOUNDRIES and ScienceDesk.
Dr. Carlos Viol Barbosa will provide a training about the topic: “From Structured Queries to Data Mining: Applications of Modern Database Technologies for Experimental Research”. This will include:
Flexible data structures for materials
Novel tools for the extraction of patterns from experimental spectroscopy data
Learning from the interaction with users
In case of interest, please drop us a short note or find the link with the complete program here (https://www.dgm.de/)
adminHappy New Year 2019: Continuing education on material analytics and artificial intelligence (AI) in May
ScienceDesk participated at the International Conference on Caloric Cooling in Darmstadt between 16th and 20th September. We showed how to translate research data from caloric cooling approaches and thermomagnetic energy conversion into digital research data.
Together with the other sponsors and exhibitors, such as Toyota, evico magnetics, and Fraunhofer ISC, we discovered promising research ideas and vivid discussions. We would like to thank Prof. Dr. Oliver Gutfleisch and his team from Technische Universität Darmstadt for the opportunity and all visitors for the interesting talks.
Here is what you can expect: “Platforms for science tend to replicate paper-based documentation and folder-based organization on the computer screen. The scientific process, from data acquisition to publication, could be greatly improved by recent advances in network theory, artificial intelligence and data mining. Stepping in this direction, we created a scientific database system where the experimental data is homogenized and deep-indexed. This allows the autonomous exploration of data to identify numeric features and correlations in large material datasets. ScienceDesk’s final goal is to create a friendly environment for data ingestion into research cycles, where correlated data, samples, experiments and articles can be efficiently retrieved.”