PhD Researcher in Nanotechnology
vor 3 Wochen
The Faculty of Mechanical Science and Engineering at the Institute of Materials Science is seeking a dedicated Research Associate / PhD Student (m/w/d) to join the Chair of Materials Science and Nanotechnology. This role is intended for individuals aiming to enhance their academic qualifications, typically through a PhD program.
The appointment is initially for a period of 36 months, with the possibility of extension, and is governed by the Fixed Term Research Contracts Act. We promote a work environment that supports a balance between family and career, making this position suitable for candidates interested in part-time opportunities.
Key Responsibilities:
The successful candidate will engage in research within the Collaborative Research Center CRC1415, focusing on the chemistry of synthetic 2D materials. The primary objective is to integrate quantum mechanical (QM) methods with advanced machine learning (ML) techniques to analyze and predict the electronic and magnetic characteristics of building blocks in two-dimensional polymers.
This research will contribute to establishing data-driven design principles, which will be incorporated into generative AI frameworks aimed at creating innovative 2D polymers with specific QM properties for applications in catalysis and energy storage.
Collaboration with synthetic chemists will be essential to validate the synthesis of new materials, fostering an interdisciplinary research environment. The role will involve conducting extensive QM calculations utilizing density functional theory and developing Python-based tools for data analysis. Additionally, enhancing ML-based computational workflows to investigate structure-property and property-property relationships will be a significant aspect of the work.
Qualifications:
Applicants should possess a university degree (Master/Diploma) in physics, chemistry, materials science, engineering, or a related field. Proficiency and practical experience in quantum mechanical methods and machine learning approaches, including force field development and generative models, are essential.
The ideal candidate will demonstrate a strong commitment to research, possess interdisciplinary thinking skills, and be capable of working both collaboratively and independently. Excellent communication and writing skills in English are required. Familiarity with programming languages such as Fortran, Python, and PyTorch is highly advantageous. Previous computational experience in research projects involving organic molecules and materials is beneficial but not mandatory.
We encourage applications from all qualified individuals, including women and candidates with disabilities, as we strive to enhance diversity within our academic community.
Application Process:
Interested candidates should submit a comprehensive application, including a Curriculum Vitae, a motivation letter, academic transcripts, and a recommendation letter from a research scientist. Please ensure that all documents are submitted as a single PDF file.
Data Protection Notice:
Information regarding data protection rights and the purpose of data processing is available on our website.
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