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PhD Student/ Research Assistant (f/m/d) in Design optimization for printed neuromorphic computing and machine learning classifiers


Institut für Technische Informatik (ITEC)


Printed electronics (PE) based on additive manufacturing processes holds promise of meeting cost and conformity needs of application domains which cannot be targeted by conventional silicon VLSI technologies. The notion of printable electronics encompasses any printing technologies or processes to create electronic devices, circuits and systems. Printed electronics will complement rather than compete with silicon-based electronics. Due to constraints of PE and requirements of target applications, realization of machine learning classifiers in PE is a promising alternative to conventional processor architectures. The goal of this project is to perform design space exploration and hardware-aware automated machine (AutoML) to optimize the hardware design of machine learning classifiers for printed electronics.


as soon as possible

Persönliche Qualifikation

  • The applicants should hold a university degree (Diploma or Masters) in the areas of Computer Science or Electrical Engineering and should also have strong English communication skills (both Oral and Writing).
  • Suitable candidates must possess a strong willingness for research exploration, independence, self-learning, creativity, teamwork and communication skills as well as the willingness in the preparation of research proposals.
  • Basic knowledge in logic design and design space exploration and experience in machine learning, AutoML, Neural Architectural Search (NAS) are necessary.


The remuneration occurs on the basis of the wage agreement of the civil service in TV-L E13, depending on the fulfillment of professional and personal requirements.

Contract duration

limited to four years

Application up to

September 14, 2022

Contact person in line-management

For further information, please contact Prof. Mehdi Tahoori, email:


Please send your application including a cover letter, your CV, MS and BS transcripts, and all certificates/referees in electronic form to:

vacancy number: 2177/2022

We prefer to balance the number of employees (f/m/d). Therefore, we kindly ask female applicants to apply for this job.

Recognized severely disabled persons will be preferred if they are equally qualified.