Research and teaching tasks in the research group Distributed Operating Systems (DOS). Development and experimental evaluation of methods for accounting and forecasting the energy, carbon, and water footprints of emerging AI systems. Design of optimization and control strategies that adapt distributed computing systems based on sustainability signals such as carbon intensity, renewable energy availability, and water consumption. Further development of our co-simulation tools for computing infrastructures and energy systems to analyze and evaluate the behavior and sustainability impacts of large-scale AI deployments. Publication of research results in international conferences.

• Successfully completed academic degree (Master, Diplom or equivalent) and a PhD in Computer Science or a closely related field.
• Proven track record of scientific publications at international conferences.
• Solid research experience in energy- and carbon-aware computing, sustainability accounting, or the resource-efficient operation of large-scale computing infrastructures.
• Strong background in machine learning and artificial intelligence, with particular emphasis on AI systems for training and inference at scale.
• Experience with performance, efficiency, and resource modeling of modern AI workloads, including accelerator-based computing and large-scale model deployment.
• Expertise in data-driven modeling, forecasting, and optimization methods for evaluating and controlling system behavior under sustainability constraints.
• Excellent programming skills in Python and experience with scientific computing, machine learning frameworks (e.g., PyTorch), and optimization tools.
• Experience with scalable data analysis, distributed systems, and cloud or data center infrastructures.
• Familiarity with energy systems, carbon accounting methodologies, or sustainability metrics is highly desirable.
• Strong experience in publishing and presenting scientific results as well as in teaching and mentoring students.
• The ability to teach in German and/or in English is required; willingness to acquire the respective missing language skills.
• Ability to conduct independent scientific work and to collaborate successfully with interdisciplinary and industry partners is desirable.

Please send your written application with the reference number and the usual documents (CV, list of grades, language certificates) only by email (single pdf file, max. 5 MB) by email to Prof. Dr. Odej Kao (odej.kao@tu-berlin.de).

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. Applications from people of all nationalities and with a migration background are very welcome.