Alexander Goscinski
I am currently working as a Software Engineer at the Paul Scherrer Institut in Switzerland focusing on the development of AiiDA a workflow engine specialized on managing high-throughput calculations. Before this I earned my PhD at the École Polytechnique Fédérale de Lausanne (EPFL) in Materials Science and Engineering in the Laboratory of Computational Science and Modeling. My research focused on studying features of machine learning models used for the prediction of atomistic properties. I am passionate about developing software that helps researchers to push the boundaries of materials science research. In my free time, I enjoy tennis and running outdoors. In addition to my research, I am also skilled in programming languages such as Python and C and am interested in diving more into Rust and F#. I have experience managing high-performance computing systems and have contributed to several open-source software projects in the field of computational materials science. I am always looking for opportunities to collaborate with others and learn from their experiences.
Paul Scherrer Institut
Git*hub|lab –Session
In computational science, different software packages are often glued together as scripts to perform numerical experiments. With increasing complexity, these scripts become unmaintainable, prone to crashes, hard to scale up and to collaborate on. AiiDA solves these problems via a powerful workflow engine and by keeping provenance for the entire workflow. In this tutorial, we learn how to create dynamic workflows combining together different executables that automatically can restart from failed runs and reuse results from completed calculations via caching.