Joachim Bucher

Joachim Bucher has a diverse work experience spanning over various roles and companies. Joachim started their career as a trainee at Heidelberg in 1997. Later, in 2002, they worked as a trainee at Evonik Degussa. Then, they joined IBVT, University of Stuttgart as a research assistant from 2004 to 2010. Joachim continued their research assistant role at CSB, University of Stuttgart in 2010.

In 2011, Joachim became a project manager in pharma testing at an undisclosed company where they worked until 2016. From 2016 to 2018, they served as a project engineer for bioprocess optimization at Yokogawa Insilico Biotechnology GmbH. During this time, Joachim was involved in computational process optimization, data science analysis, and media design using Python and R.

Currently, Joachim holds the position of Senior Data Scientist at Yokogawa Insilico Biotechnology GmbH. Joachim leads several customer projects on predictive manufacturing and contributes to building Digital Twins for bioprocess analysis and optimization. Additionally, they provide consultancy to multiple US customers.

Joachim Bucher completed their education in a chronological manner. Joachim began their studies at Michelberggymnasium from 1987 to 1996, obtaining their Abitur degree. Following that, from 1997 to 2003, they attended the University of Stuttgart and earned a Diplom Engineer degree in Chemical Engineering. Finally, they pursued further education at the University of Stuttgart from 2004 to 2011, completing their Doctor of Philosophy (PhD) degree in Chemical Engineering.

In addition to their formal education, Bucher has obtained various certifications from DataCamp. Joachim acquired certifications in topics such as Advanced Deep Learning with Keras, Feature Engineering for NLP in Python, Hyperparameter Tuning in Python, Image Processing in Python, Image Processing with Keras in Python, Introduction to Deep Learning with Keras, Introduction to Natural Language Processing in Python, Introduction to PySpark, Introduction to TensorFlow in Python, Machine Learning Scientist with Python, Machine Learning with PySpark, Winning a Kaggle Competition in Python, Cluster Analysis in Python, Dimensionality Reduction in Python, Extreme Gradient Boosting with XGBoost, Feature Engineering for Machine Learning in Python, Linear Classifiers in Python, Machine Learning for Time Series Data in Python, Model Validation in Python, and Preprocessing for Machine Learning in Python. These certifications were obtained in the months of April and May 2020.