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AI Engineer with ML/NLP expertise. Specialist in RAG systems and end-to-end ML solutions. Currently driving AI innovation at TeamViewer.
AI & NLP Engineer with Master's in AI. Focused on ML solutions, data pipelines, and process automation. Team player ready for innovative R&D projects.
Passionate about AI trends. Regular reader on Medium to stay at the cutting edge of the field.
Mohammad Alnajdawi
najdawimohamed@gmail.com
+43 681 84316564
Linz, Upper Austria, Austria
Experience cutting-edge AI technology! My intelligent chatbot knows everything about my background, projects, and expertise. Ask anything!
Advanced AI technology with deep knowledge of my professional background
Linz, Upper Austria, Austria
Vienna, Austria
Johannes Kepler University
Specialized in Natural Language Processing and Machine Learning applications.
University of Jordan
Focused on software engineering and data structures.
DataCamp
Issued 2020
Learned how to import, clean, manipulate, and visualize data. Performed Supervised and Unsupervised Machine Learning Algorithms to process data for modeling, visualize models and assess their performance.
Coursera - DeepLearning.AI
Issued 2021
Five-course specialization covering neural networks, optimization algorithms, and applying ML at scale.
Authors:
Natasha Trajkovska, Michael Roiss, Sophie Bauernfeind, Mohammad Alnajdawi, Simone Sandler, Daniel Herzmanek, Matthias Winkler, Michael Haider, Oliver Krauss
While adherence to clinical guidelines improves the quality and consistency of care, personalized healthcare also requires a deep understanding of individual disease models and treatment plans. The structured preparation of medical routine data in a certain clinical context, e.g. a treatment pathway outlined in a medical guideline, is currently a significant challenge in healthcare informatics.
Contributing to the advancement of healthcare informatics through innovative approaches to medical data processing and clinical decision support systems.
Legal document platform enabling efficient insight extraction. Improved search by 40% and reduced query time by 35%.
Auto-generator of concise user session summaries. Reduced support team analysis time by 60%.
Extraction and transformation of unstructured medical data enabling predictive modeling.
Web application that automatically generates quizzes from PDFs or links, streamlining learning assessment.