Postdoctoral Fellow in Biochemistry

About the project

The project

• addresses challenges in biocatalysis by leveraging machine learning to streamline the enzyme discovery process, accelerating innovation in pharmaceutical and chemical industries.
• focuses on predicting and optimizing enzymes for industrial biocatalysis using machine learning models.
• aims to develop an in-silico method to identify promising enzyme candidates for defined chemical transformations.
• will develop Machine learning models using sequence, structural, and functional data to predict enzyme activity and design optimized enzyme variants, which will be validated in the wet lab.

Your host: Ziemert Lab led by Prof. Nadine Ziemert, in collaboration with Pfeifer Lab led by Prof. Dr. Nico Pfeifer at the University of Tübingen.

Your collaborating scientists: Alexander Fejzagic, PostDoc at Boehringer Ingelheim, and Marco Santagostino, Distinguished Research Fellow at Boehringer Ingelheim.

Your profile

The ideal candidate will bring:

• and Ph.D. or equivalent in Cheminformatics, Biochemistry, Enzymology, Structural Biology, Machine Learning, Bioinformatics, Natural Products Biosynthesis, or a related discipline
• demonstrated experience in bioinformatics, data science, machine learning / deep learning, and enzymology
• a competitive track record of scientific publications in high-impact venues
• a keen interest in interdisciplinary work
• strong communication skills and experience
• collaborating in interdisciplinary and cross-sector teams are advantageous
• the ability to work both independently and as part of a collaborative team

Our offer

What this position offers you:

• exciting research at Europe's leading AI and Drug Discovery Campus
• cooperation with academic researchers as well as a research-driven global pharmaceutical company
• a dynamic and supportive work atmosphere
• remuneration in accordance with the TV-L (collective agreement for public employees of the German federal states) as well as all corresponding benefits
• Career mentoring
• to gain leadership experience by opportunity supervising research assistants
• potential for travel to conferences and professional development workshops

We value diversity in science, and particularly look forward to receiving applications from women, non-binary people, and researchers from underrepresented groups across cultures, genders, ethnicities, and lifestyles. We actively promote the compatibility of science, work, studies, family life, and care work. In case of equal qualification and experience, physically challenged applicants are given preference.

How to apply

We invite you to apply by sending your application (including cover letter, curriculum vitae with a list of publications, certificates, and contact details of 2 academic references) with the subject “BI AI & Data Science and Machine Learning Fellowship Application” via e- mail to nadine.ziemert spam prevention@uni-tuebingen.de .

Application deadline : 15/11/2024