Start Date
Immediate
Expiry Date
13 Nov, 25
Salary
0.0
Posted On
13 Aug, 25
Experience
0 year(s) or above
Remote Job
Yes
Telecommute
Yes
Sponsor Visa
No
Skills
Bioinformatics, Research Projects, Teaching, Research
Industry
Education Management
SCIENTIFIC ENVIRONMENT
The professorship will be assigned to the TUM Campus Straubing for Biotechnology and Sustainability and will play a central role in interdisciplinary and transdisciplinary research at the interface between data science, life sciences, and bioeconomics. The professor will closely cooperate with the TUM School of Computation, Information and Technology (CIT), the TUM School of Life Sciences (LS), and with the Excellence Cluster BioSysteM.
QUALIFICATIONS
We are looking for candidates who have demonstrated excellent achievements in research and teaching in an internationally recognized scientific environment, relative to the relevant career level (please see www.tum.de/en/faculty-recruiting-faq/ for further information).
A university degree in bioinformatics and an outstanding doctoral degree or equivalent scientific qualification, as well as pedagogical aptitude, are prerequisites. Substantial research experience abroad is expected. Successful applicants will have the proven ability to acquire and to lead cooperative research projects and to attracting third-party funding.
The responsibilities include research and teaching as well as the promotion of early-career scientists. We seek to appoint an expert in the research area of Machine Learning for Sustainable Processes and Materials with a focus on data-driven methods for modeling, analyzing, and optimizing complex biological, biotechnological and agricultural systems. The main focus is on machine learning approaches, in particular statistical learning, reinforcement learning, deep learning, and computer vision, as well as the statistical and bioinformatics analysis of chemical and biological processes and systems, and to gain a deeper understanding of biochemical and biotechnological processes and systems through the integration of modern data science, machine learning and bioinformatics methods. This also includes modern in silico methods for analyzing genomic, genetic, and phenotypic data. The focus is on applying data science methods to support the transition to a sustainable bioeconomy, taking into account economic and social science aspects. Teaching responsibilities include courses in the university’s bachelor and master programs at TUMCS (Professional Profile Bioeconomy) in German and English.