Tobias Schmidt

Technical lead software devolpment at MSAID

Tobias Schmidt has a diverse work experience spanning across various industries. In 2008, they started their career as a Research Assistant at the ifab - Institut für Arbeitswissenschaft und Betriebsorganisation. Tobias then moved on to the Karlsruhe Institute of Technology (KIT) / University of Karlsruhe (TH) as a Research Assistant, where they were involved in modeling protein structures. In 2011, they joined Roche as a Student Trainee, and their responsibilities included assisting with various tasks. After that, in 2014, they worked as a Research Assistant at the HelmholtzZentrum München, where they monitored applications for the German Mouse Clinic. Tobias then transitioned to hybris software in 2015, where they held a position as a Student Trainee. Tobias then pursued a Ph.D at TU Munich in 2016. In 2019, they became a Co-Founder and Technical Lead of Software Development at MSAID. It is evident from their work experience that they have a strong background in research and has gained valuable experience in the fields of software development and project management.

Tobias Schmidt has a strong background in scientific and mathematical studies. Tobias began their education in 2007 at the Karlsruhe Institute of Technology (KIT) where they pursued a Vordiplom degree in Mathematik (mit Nebenfach Informatik) until 2010. After completing this initial phase, they focused their interests on molecular biotechnology and enrolled at the Technical University of Munich in 2010. Here, they obtained their Bachelor's degree in 2013. Tobias furthered their education at the same university and completed their Master's degree in Molecular biotechnology in 2015.

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Munich, Germany

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MSAID

MSAID transforms the way scientists analyze proteomics data. MSAID is a bioinformatics spin-off from the Technical University of Munich, Germany and is located at the high-tech-center gate, which is part of the TUM campus in Garching. The company was founded by an interdisciplinary team of scientists with the vision to provide better computational solutions to the field of proteomics. The team has an exceptionally strong track record and long-standing expertise in the acquisition, analysis, and interpretation of proteomic data. Our ambition is to replace current algorithms for proteomics with powerful, AI-based solutions, thereby paving the way for a smarter, deeper, and more reliable way of interrogating proteomic data.


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11-50

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