Job offer

• Development of ML-based tools to analyse data from different surface sensitive scattering techniques (X-ray Reflectivity (XRR), Grazing-Incidence Wide-Angle X-ray Scattering (GIWAXS) and others)
• Contribution to the scattering data analysis and support of data/metadata formats developed in the group
• Integration of the developed software into the computational environments and data handling routines
• Presentation of the scientific results on conferences and in publications

Chemistry » Physical chemistry

Computer science » Programming

Computer science » Systems design

Researcher Profile First Stage Researcher (R1), Master Degree or equivalent

Research Field Chemistry

Education Level Master Degree or equivalent

Research Field Computer science

Education Level Master Degree or equivalent, Candidates should have good communication skills and motivation to familiarize themselves with new subject areas. The role requires both independent work and effective collaboration, particularly during measurement campaigns at synchrotron and neutron facilities. Experience with programming languages such as Python is advantageous. Participation in teaching activities (e.g., supervising practical courses or tutorials) may also be possible. While knowledge of German is not required, it will be considered a plus. Specific Requirements

• Master's degree in physics or chemistry, computer science or equivalent
• Interest in Physics and Machine Learning
• Good written and spoken English
• Ability to work both independently and in a team
• Programming skills (Python) and acquaintance with modern machine learning frameworks (PyTorch/JAX) are strong advantages

Languages ENGLISH

The positions offered provide access to challenging and interdisciplinary projects integrated into large national and European research consortia such as the DAPHNE (DAta for PHoton and Neutron Experiments) NFDI consortium. The group offers well-equipped laboratories, a highly collaborative international environment and membership of the Cluster of Excellence "Machine Learning: New Perspectives for Science", which is funded by the DFG and is located at the University of Tübingen. Students in the group receive excellent training and supervision and the opportunity to conduct research at large-scale international facilities, such as synchrotron and neutron sources. Details of the research activities as well as publications and background information are available on the group website: https://www.soft-matter.uni-tuebingen.de Eligibility criteria

The University seeks to raise the number of women in research and teaching and therefore emphatically calls on qualified women to apply. Disabled candidates will be given preference over other equally qualified applicants. The university is committed to equal opportunities and diversity. It therefore takes individual situations into account and asks for relevant information. The employment will be handled by the central administration of the University of Tübingen. Selection process

The research group of Prof. Dr. Dr. h.c. Frank Schreiber at the University of Tübingen works on the physics of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering experiments. For more information, visit our web page www.soft-matter.uni-tuebingen.de, The University of Tübingen is one of Germany's Universities of Excellence. It provides top-level research and teaching aimed at finding solutions to future challenges in a globalized society. The University has around 28,000 students and more than 500 years of academic tradition. It is continuously placed among Germany's best in national and international rankings. Tübingen is one of the classical "university towns" in Germany. It is located next to Stuttgart - the capital of the state Baden-Württemberg. It is well-connected to the neighboring cities and the international Stuttgart airport via train lines and busses. It is known for its vibrant student life, cultural events, historical architecture and beautiful nature.