• Bio-molecular databases
• Unsupervised learning (clustering)
• Distributed/parallel computing
• Web/UI development
• Scientific and statistical computing: Python, R, C/C++
Unsupervised learning (clustering)
Scientific and statistical computing: Python, R, C/C++
Arnold graduated in Molecular Biology (2004, Comenius University in Bratislava, Slovakia). During his masters, he participated in an exchange program at Wageningen University and Research Centre (WUR) in the Netherlands and followed training in bioinformatics. His thesis focused on comparative genomics of White spot syndrome (shrimp) virus isolates, in particular on detecting genetic changes and epidemiological markers in the genomes.
Arnold continued with a PhD research on orthology-based clustering and data integration at the Laboratory of Bioinformatics, WUR (2009). In close collaboration with SURFsara, he used the Dutch Life Science Grid to compute a comprehensive map of corresponding (orthologous) genes for all species with fully sequenced genomes, which also involved the development of a fast and memory-efficient graph heuristics.
As a postdoctoral researcher he joined an e-Science project at the Department of Ecology and Evolution, University of Lausanne/Swiss Institute of Bioinformatics, aimed at genome-wide detection of positive selection across the animal kingdom using the Swiss Multi-science Computing Grid. After returning to the Netherlands, he worked at the Department of Genetics, Erasmus Medical Center, and developed bioinformatics tools to analyze mass spectrometry-based proteomics data.
Arnold joined the Netherlands eScience Center in 2015 to work on projects that require optimized data handing (e.g. using Linked Data approach), analytics and/or efficient computing. His interests include the development of efficient algorithms, user-friendly tools and databases for molecular life. sciences.