Professur für maschinelles Lernen und datengetriebene Methoden in der Chemie nach dem Thüringer Modell (w/m/d)

About the role

The position is part of the Thuringian Model collaboration between the University of Paderborn, the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), and the Center for Advanced Systems Understanding (CASUS).

Responsibilities

• Develop and apply scientific machine learning methods for electronic structure theory and chemical questions.
• Experience in developing and applying data-driven and ab-initio simulation methods.
• Creation of corresponding training data.
• Focus on the development and use of scientific, physically informed, and ab-initio data-based methods of machine learning.
• Expand the teaching portfolio to include methods of machine learning and data-driven topics.
• Participate in academic self-administration.

Requirements

• Proven experience in the development and application of theoretical chemical, data-driven, and machine learning-based methods.
• Experience in securing and managing competitive research projects.
• Interdisciplinary and international collaboration with universities and non-university research institutions.
• Minimum two years of leadership experience in an external research institution.
• Cooperation skills and adaptability to groups within the Department of Chemistry.

Qualifications

• Completed university studies, pedagogical suitability, relevant postgraduate degree, and additional habilitation-equivalent scientific achievements.
• Preference will be given to applications from women and qualified disabled persons under the Equal Treatment Act (Gesetz gegen Diskriminierung der Behinderten, GDB) and the Social Code Book (Sozialgesetzbuch, SGB) Ninth Book, if not due to personal reasons.

Skills

• Strong background in machine learning and its application in chemistry, particularly in data-driven modeling based on ab-initio methods and atomistic simulations.
• Experience with spectroscopic methods, high-performance computing resources, and collaborations with large-scale research facilities.

Benefits

No specific benefits are mentioned in the job posting.

Pay

No specific pay details are provided in the job posting.

Schedule

No specific schedule details are provided in the job posting.