Dr. Jens Settelmeier

Development of Machine Learning algorithms for mass spectrometry and (multi-) omics data to find potential biomarker.

Development of Time Series Prediction Tools for hana-ml.

Deep Learning-Based Identification of Chimeric Peptide Mass Spectrometry Data as Research Intern and Master Thesis Student at Noble Research Lab, University of Washington, Seattle.

Unsupervised Object Tracking based on Radar and Image data.

Time Series Prediction for Maintenance Forecasts of industrial oil and gas facilities to save resources. Research and Development of suitable solutions. Prototyping in Matlab and Python, using PyTorch and Tensorflow.

Teaching Assistant for Artificial Intelligence at KTH Royal Institute of Technology, Stockholm.

Several tasks in the departments of High Performance Computing, Image Processing, Mathematical Methods in Dynamics and Durability.

Duales Studium (cooparate program) B.Eng. Mechatronics - Projekt Engineering (aborted)

AI in Mass Spectrometry and Omics data.

Courses: AI in Game Design (Reinforcement Learning), Neuroscience (Molecular and Cellular), Machine Vision (Computer Vision 2D and 3D), Neural and Fuzzy Systems

Artificial Intelligence, Deep Learning, Machine Learning, Computational Biology Thesis: http://www.diva-portal.org/smash/get/diva2:1554629/FULLTEXT01.pdf

Focus on: Analysis and Stochastics Bachelor Thesis: Modellierung von Kfz-Prüfständen mit Echo State Neural Networks English title: Modelling of Automotive Test Benches Using Echo State Neural Networks. 2013-2014 Academic orientation year: visited courses in Economics, Biology, Physics, Chemistry, Electrical Engineering, Computer Science, Mathematics