Senior Scientist Postdoc /
Research Group Lead
(m/f/d)

Institute: Smart Reactors
Remuneration: EG 14  TV-L 
ID: 19725WV4
Start of employment: 1 October 2025
Application deadline: 3 August 2025
Scope: Full time, fixed-term for three years (after successful assessment extension for another three years possible)

The Collaborative Research Center (CRC) 1615 "SMART Reactors for Future Process Engineering" aims to develop reactors that autonomously and sustainably convert renewable feedstocks into a wide variety of products. To contribute to this vision, we are establishing a Junior Research Group dedicated to machine learning (ML), automation, and multiscale modeling. This group will aid the CRC 1615 with state-of-the-art ML tools tailored to the diverse needs of experimental and simulation-based subprojects. Anchored at the Institute of Process Systems Engineering, the group will collaborate closely with application-driven PIs from across the CRC. By identifying structurally similar problems and cross-cutting use cases, the group will transfer developed methods and insights throughout the center.

YOUR TASKS

• Development and adaptation of cutting-edge machine learning methods tailored to CRC 1615 use cases
  
• Coordination of collaborative research activities and scientific integration across CRC subprojects
  
• Scientific guidance of PhD students within the group
  
• Development of teaching materials and establishment of a new course in Scientific Computing and Machine Learning in Chemical Engineering
  
• Preparation of high-quality scientific publications and presentations
  

YOUR PROFILE

Requirements

• University degree in the subject of Computational Engineering, Computer Science, Data Science, Chemical Engineering, or a closely related field, doctorate
  
  

Required knowledge and personal skills

• Strong publication record and proven research experience in applying machine learning techniques to chemical engineering challenges
  
• In-depth expertise in model identification, including approaches such as symbolic regression and neural networks
  
• Experience in data-driven process optimization, including Bayesian Optimization and active learning
• Excellent communication and collaboration skills; demonstrated ability to work independently and lead research activities or teams

OUR OFFER

• The opportunity to pursue further academic qualification (e.g., habilitation)
  
• Close integration with a DFG-funded research center working and connection to the research initiative Machine Learning in Engineering (MLE)
• Participation in national and international conferences, including options for research stays abroad
  
• A job in an interesting, friendly, supportive and appreciative working environment
• Intensive induction and onboarding
  
• 30 day vacation per year
  

At the heart of TU Hamburg’s research, teaching and transfer of technology is the guiding principle of developing technology for people. The TU Hamburg sees itself in this context as a family-friendly and sustainable university with high performance and quality standards that strives for excellence in all its research fields. Interdisciplinarity, innovation, regionality and internationality are binding principles in our actions.

With currently around 8.000 students, 110 professors and 1.650 employees, the TU Hamburg is characterised by short decision-making processes and close cooperation between the board, the institutes, the deans of studies, the research areas and the administration. We identify ourselves with a modern leadership culture and cultivate appreciative interaction.

For further information please contact Prof. Mirko Skiborowski, Tel.-Nr. +49 40 42878 3041, Email: mirko.skiborowski@tuhh.de.