We are looking for post-docs in Responsible Deep Learning*!

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Ok, we all work remotely at the moment anyway. But this chair will be waiting for you.

*Modeling lung imaging data using deep neural networks following ResponsibleAI principles. No worries, you don’t need to have prior experience with medical data.

Who we are

ML2DataLab is a research group lead by Przemysław Biecek that brings together research oriented individuals interested in machine learning from the Warsaw University of Technology and the University of Warsaw.

We love open software. We strive to work on current challenges such as ExplainableAI (EMA, dalex, arena, explainable meta learning), COVID (epidemics, XAI for lung images), robustness of NLP models (WildNLP, NER) applications in finance or medical domains (scoring, segmentation).

Who are we looking for

  • Machine learning enthusiast interested in serious research work.
  • Candidate with a PhD, ideally in mathematics, statistics, or computer science (other fields are also welcomed).
  • Has published work at top ML/AI conferences/journals.
  • Active person who will strengthen our potential related to deep learning.
  • Communicative person, open to applied, often very interdisciplinary work.

What we offer

  • Stable salary for up to two years (twice of the average salary in Poland).
  • Access to modern research infrastructure (computing infrastructure, 4xA100 computing server).
  • Installation grant (for up to two juniors).
  • Opportunity for further development as a research associate.
  • Great atmosphere in a team that values cooperation and is open for bold ideas.

Deadline: end of February.

If you have any questions, please send them to Przemysław Biecek: https://www.linkedin.com/in/pbiecek/

More info: https://euraxess.ec.europa.eu/jobs/599581 (ENG), https://www.bip.pw.edu.pl/content/download/45641/445557/file/Konkurs.pdf (PL and ENG)

We are looking for post-docs in Responsible Deep Learning*! was originally published in ResponsibleML on Medium, where people are continuing the conversation by highlighting and responding to this story.

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