Aktiviere Job-Benachrichtigungen per E-Mail!

Doctoral Researcher (m/f/d) in AI for Additive Manufacturing

TN Germany

München

Vor Ort

EUR 45.000 - 70.000

Vollzeit

Vor 17 Tagen

Erhöhe deine Chancen auf ein Interview

Erstelle einen auf die Position zugeschnittenen Lebenslauf, um deine Erfolgsquote zu erhöhen.

Zusammenfassung

An innovative academic institution is seeking a Doctoral Researcher specializing in AI for Additive Manufacturing. This exciting role involves researching and developing cutting-edge AI/ML methods to predict outcomes in construction processes. The successful candidate will have the opportunity to pursue a Ph.D. while working in a collaborative environment that promotes scientific inquiry and innovation. With a focus on geometric modeling and AI applications, this position offers a chance to contribute to impactful research that addresses current challenges in the field. Enjoy a vibrant life in Munich while advancing your career in a supportive team.

Leistungen

Excellent research environment
Supervision and networking opportunities
Friendly and committed team
High quality of life in Munich

Qualifikationen

  • Master's or Diploma in Computer Science or Engineering required.
  • Strong background in Machine Learning and AI is essential.

Aufgaben

  • Research and develop AI/ML methods for Additive Manufacturing.
  • Publish and present scientific results at conferences.

Kenntnisse

Machine Learning
Artificial Intelligence
TensorFlow
PyTorch
Keras

Ausbildung

Master's or Diploma in Computer Science
Engineering

Tools

AI frameworks

Jobbeschreibung

Social network you want to login/join with:

Doctoral Researcher (m/f/d) in AI for Additive Manufacturing, Munich

col-narrow-left

Client:

Technical University of Munich

Location:
Job Category:

Manufacturing

-

EU work permit required:

Yes

col-narrow-right

Job Reference:

06f791684028

Job Views:

2

Posted:

28.04.2025

Expiry Date:

12.06.2025

col-wide

Job Description:

EU EIC Pathfinder Project AM2FM

Doctoral Researcher (m/f/d) in AI for Additive Manufacturing

The Chair of Computational Modeling and Simulation (CMS) at the Technical University of Munich (TUM) invites applications for a doctoral position in the topic “Learning by Printing,” focusing on AI applied to Additive Manufacturing in Construction. The successful candidate can pursue a Ph.D. and will be remunerated according to TV-L E13 rates. Availability is expected from September 1st, 2025.

The Chair of Computational Modeling and Simulation is part of the TUM School of Engineering and Design, focusing on computer-based development of engineering products, including planning and realization of built facilities using computational tools.

Research areas include geometric modeling, analysis methods, Building Information Modeling, construction process simulation, and AI applications in engineering.

Project Description:

The project investigates how AI can predict the quality and properties of Additive Manufacturing outputs in Construction, addressing current limitations related to geometric and property variations.

Responsibilities:

  • Research, develop, and evaluate AI/ML methods for predicting outcomes of Additive Manufacturing.
  • Conduct literature reviews.
  • Publish and present scientific results at international conferences and journals.
  • Contribute to group teaching and research activities.

Requirements:

  • Master's or Diploma in Computer Science, Engineering, or a related field.
  • Background in Machine Learning and Artificial Intelligence.
  • Proficiency with AI frameworks like TensorFlow, PyTorch, Keras.

Desirable:

  • Scientific publications.
  • Experience with ML models such as CNNs, LSTMs, Attention Networks, GNNs.

We Offer:

  • Excellent research environment with supervision and networking opportunities.
  • Friendly and committed team.
  • Remuneration according to pay group E13 (TV-L).
  • High quality of life and leisure activities in Munich.
Hol dir deinen kostenlosen, vertraulichen Lebenslauf-Check.
eine PDF-, DOC-, DOCX-, ODT- oder PAGES-Datei bis zu 5 MB per Drag & Drop ablegen.