Henrik Junkerkalefeld

• Statistical analysis focused on the identification of rare particle decays in large high-dimensional data sets with Python. • Development of complex data preprocessing pipelines, hypothesis testing and determination of confidence intervals, application of Boosted Decision Trees. • Leading and coordinating a five-member analysis team within a collaboration of 1000+ researchers; strategic planning and supervision of one master’s and two bachelor’s theses.

• Support of various MSc and BSc lectures and laboratory courses, including the creation of exercise assignments and exams • Experienced in teaching complex content to diverse audience

Software development of deep machine-learning algorithms to automatize semantic segmentation with different sensor technologies using Tensorflow, Keras, and Python.

Theoretical quantification and optimization of the achievable experimental precision of novel measurement methods of antiproton properties with software like Mathematica.

Theoretische und experimentelle Teilchenphysik, Datenanalyse