Christoph Hiemenz

Natural Language Processing (NLP), Text Classification, NER, Search engines, Customer Relationship Analytics, Timeseries Analysis, Customer Classification, Cohort Analysis, Customer Training, Consulting, AWS & Azure Machine Learning, Model Training & Deployment Technologies: Python, Deep Learning, PyTorch, Docker, AWS SageMaker, Azure Machine Learning SDK, Microsoft Language Understanding Intelligent Service (LUIS), GitLab, multivariate Statistics, RESTful services, R, Tableau, KNIME, SynopAnalyzer

Beratung zur Konfiguration und Programmierung von maßgeschneiderten Laborinformationsdatenbanken (LIMS), elektronischen Laborbüchern und elektronischen Arbeitsanweisungen in global tätigen Pharmaunternehmen. Erstellung von "Use Cases" und "Funktionalen Requirements." Konfiguration von Software Demonstrationen. Leitung mehrtägiger Produktschulungen zur Datenbankadministration. Unterstützung von Vertriebsaktivitäten durch Softwarepräsentationen und Softwarekonfigurationen auf Fachkonferenzen.

Establishment of proteinconjugation reactions on the E.coli cell surface supervisor: Prof. Dr. Stefan Ståhl (Division of protein engineering)

Characterization of lysosomal Ca2+ signal transduction pathways via photouncaging, photocrosslinkage and CuAAC click chemistry of trifunctional sphingolipids supervisor: Dr. Doris Höglinger

Teaching the foundations of organic chemistry to bachelor students as a student assistant.

Ordinary differential equation modeling of light-inducible nuclear protein import and export systems (optogenetics) supervisor: Dr. Dr. Stefan Kallenberger

Using bioluminescence to control optogenetic proteins and establishment of optogenetic membrane localization in E.coli. supervisor: Prof. Dr. Barbara Di Ventura

Thesis: One kinase is not enough: Peptide arrays for global kinome profiling Grade: 1.1* (*In a system were 1.0 is the best and 4.0 is the worst grade) Total grade: 1.0*

Thesis: Fluorophore dimers as turn-on probes to image RNA in vivo Grade: 1.0* Total grade: 1.1*