Paid [Deep Learning and Explainable AI]

  • Bachelor theses (706.170, 706.171, 706.172 etc.)
  • Master projects (706.501, 706.502, 706.505)
  • Master theses (706.997, 706.998)

Frameworks: Python (tensorflow, keras, PyTorch) + OpenCV

Topic: Medical Image Processing (Gigapixel images)

  • object detection, scene parsing
  • caption generation (NLP)
  • [variational] autoencoders
  • meta-learning
  • eXplainableAI methods (e.g. heatmapping, …)

Python nerds in machine learning/explainable AI, please download pdf (93 kB) Explainable-AI-Job-Posting-HCAI-Lab-Holzinger-Group

*) EUR 4,123,20  Basis +  EUR 2,061,60  Bonus if the grade of your thesis is “very good (1)”

Studies:

  • Computer Science
  • Software Engineering
  • Information and Computer Engineering

*) the basis is the FWF “Studentische Mitarbeiterin, Studentischer Mitarbeiter”, but individual adaptions are possible

send an motivation letter with CV to
eMail: anna.saranti AT human-centered.ai, a.holzinger AT tugraz.at

https://human-centered.ai/open-positions/

Doctorate/PhD positions

available in explainable AI/machine learning for Gigapixel medical images.
Please send one e-Mail containing one single pdf file containing

  • a short motivation statement with your goals in explainable AI/machine learning and why you want to work with us;
  • your catalog of elected courses (Fächerkatalog)
  • your curriclum vitae (cv)

to this e-Mail address: a.holzinger AT human-centered.ai

Note: if you send your eMail please make sure to put 20D2019G in the header to make sure that the e-Mail goes through our filter, all other e-mails will not pass the automatic AI filter system. Thank you for reading to the end and following this paragraph.

Our research seminar provides more relevant information:
https://human-centered.ai/hcai-research-seminar-2020/

SP.67 OPEN JOBS
Status
open
Additional Information

The Human-Centered AI Lab (Holzinger group) is devoted to the guiding principle: “Science is to test crazy ideas – Engineering is to put these ideas into Business” and dedicated to contribute to the international research community towards ethical responsible machine learning.