Tenure-Track Assistant Professorship for Computational Methods in Material Science and Chemical Science
Tenure-Track Assistant Professorship for Computational Methods in Material Science and Chemical Science
TU Graz - Graz University of Technology
Graz, Austria
For this position, we are seeking a highly qualified and scientifically excellent person who has committed her/his/their research on advanced computational techniques to model and simulate materials and chemical systems, by implementing machine learning approaches and/or big data mining in the field of materials chemistry or physical chemistry. The selected person will collaborate closely with experimentalists including materials scientists and chemists to bridge the gap between theory and experimentation, driving innovation and problem-solving. The position is promoted by the interfaculty Field of Expertise (FoE) “Advanced Materials Science” (AMS) and thus a multidisciplinary research strategy is highly favored.
The scientific focus of the candidate should strengthen and complement one of the strategic themes of the Field of Expertise Advanced Materials Science focusing on energy materials, sensor materials, thin films, soft matter, semiconductors, porous materials, functional (bio-based/bio-degradable) polymeric materials, or chemical aspects enabling the progress of advanced materials.
In addition, we expect willingness to engage in interdisciplinary cooperation inside and outside of TU Graz (e.g. NAWI Graz), and cooperation across faculty borders within the TU Graz Field of Expertise „Advanced Materials Science”.
Employment Requirements
- PhD-level university education in disciplines relevant for the position.
Selection Criteria
- Outstanding achievements in research, excellent publication record and international reputation;
- International experience in form of a PhD or Post-Doc;
- Experience in the design and implementation of international research projects as well as willingness and ability to establish and lead a research group;
- Enthusiasm for excellent teaching, teaching experience as well as the ability and willingness to teach in all curricular stages (Bachelor, Master, Doctorate), supervise theses and promote young scientists.
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