Research Assistant

The project aims to develop FAIR-compliant hybrid modelling frameworks tightly integrated with automated, high-throughput robotic laboratories for biotechnology. By combining mechanistic, statistical, and machine-learning models with automated experimental execution, the project will enable traceable, reproducible, and metadata-rich experimental planning. The research will integrate MLOps pipelines for model versioning, deployment, and monitoring, and will support multi-objective optimisation and multi-criteria decision-making for sustainable and efficient Single Cell Protein (SCP) production processes.
The candidate will operate at the intersection of biotechnology, robotics, data science, and sustainability, contributing to a new generation of autonomous laboratories where models and experiments are fully FAIR-compliant, reproducible, and capable of guiding high-throughput experimentation with sustainability considerations.

There is the possibility of pursuing a doctorate.

• Candidates cannot hold a doctoral degree
• Successfully completed academic degree (Master's, Diploma or equivalent) in physics, process engineering, data science or a related field.
• Compliance with the MSCA Mobility Rule: the applicant must not have resided or carried out their main activity in Germany for more than 12 months in the three years prior to recruitment.
• Knowledge of IoT frameworks and automation platforms such as Node-RED, MQTT, OPC-UA, or comparable technologies.
• Good knowledge of German and/or English required; willingness to acquire the respective missing language skills.
• Strong Python programming skills, including deployment in real-time or automated environments (desirable).
• Knowledge of differential equation systems and dynamic modeling (desirable).
• Experience with workflow orchestration and scheduling tools (e.g. Airflow, Cylc) (desirable).
• Excellent English communication and presentation skills (desirable).
• Strong organizational abilities, interdisciplinary collaboration experience, and a creative, problem-solving mindset (desirable).
• Demonstrated experience with FAIR data principles (desirable).
• Practical knowledge of MLOps principles, including model versioning, deployment, monitoring, and continuous learning (desirable).

Please send your application with the usual documents (curriculum vitae, overview of grades/transcripts and letter of application, summarized in a PDF document, max. 5 MB) by e-mail to the dean of the faculty Dr. M. Nicolas Cruz Bournazou at bioprocess-TB-office@win.tu-berlin.de, quoting the reference number.

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.