The position is part of the binational DFG project "Combinatorial development of efficient thermoelectric half-Heusler materials using machine learning, DFT and high-throughput experiments", which is being carried out in cooperation between the DLR Institute of Materials Research, the University of Montpellier and the Ruhr University Bochum. The aim of the project is to develop highly effective thermoelectric materials based on environmentally friendly and readily available materials. In particular, the aim is to synthesise new compositions, establish composition-microstructure-property relationships and further develop high-throughput measurement and evaluation methods.
Your tasks
synthesis of thermoelectric semiconductor materials from the class of half-Heusler compounds using melting or powder metallurgical processes
characterisation of thermoelectric samples with regard to microstructure (SEM, EDX, XRD), composition and integral electrical and thermal transport properties
local determination of the transport properties of graded or inhomogeneous samples, further development of existing measurement systems and analysis tools
further development of effective media models for the consideration of secondary phases in transport modelling and development of transport models taking into account the electronic band structure based on the measurement results
analysing the results and publishing them in conference papers and scientific articles
supervision of internships, bachelor's and master's theses
writing of a PhD thesis
Your profile
master's or diploma degree in a natural science or engineering field/subject, e.g. materials science, physics, chemistry or comparable
very good knowledge of written and spoken English
laboratory experience in the synthesis of inorganic compound semiconductors and experience in the functional and microstructural characterisation of functional materials (SEM, XRD, EDX, AFM...)
knowledge of solid-state physics, semiconductor physics, solid-state chemistry
ability to work in an international team of scientists, technicians, students and international co-operation partners
experience in automated data processing
experience in the use of AI-based tools and basic experience in measurement technology