Internship for robustness/adversarial deep learning

neues Angebot (05/03/2020)

stellenbeschreibung

Job Description Company Description Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch. The Robert Bosch GmbH is looking forward to your application! Job Description * Help shape the future:

The goal of your internship is to evaluate the robustness of classical and hybrid deep learning based approaches for computer vision from application areas such as autonomous driving

* Create Something New

:
you develop and deploy novel algorithms in the field of robustness/adversarial deep learning for computer vision. furthermore you implement and evaluate them using public data or bosch data. * experience cooperation:

you conduct technical discussions and create new ideas within the existing deep learning research team. * take responsibility:

besides you are responsible of the clean, efficient and well-documented implementation of new deep learning methods. * networked communication:

optionally you publish a research paper in a tier-one ai conference. qualifications * education:

master studies in the field of computer science, data science, electrical engineering or mathematics with good marks * character:

communicative, self-confident and innovative * working practice:

structured, goal-oriented and reliable * experience and knowledge:
competent handling ( 2+ years) of programming experience preferably in python, some programming experience in with deep learning frameworks (pytorch, keras, or tensorflow), some familiarity with linux as well as ideally practical experience in deep learning algorithms and principles * enthusiasm:

profound interest in deep learning or machine learning research passion of shaping future autonomous driving and making our deep learning detection algorithms more robust and safe * language:

fluent in english additional information start:

april 2020 duration:
6 months requirement for this internship is the enrollment at university. please attach a motivation letter, your cv, transcript of records, enrollment certificate, examination regulations and if indicated a valid work and residence permit. need further information about the job? nicole finnie (business department) +49 176 1330058 volker fischer (business department) +49 176 30757213 qualifications:

education:

master studies in the field of computer science, data science, electrical engineering or mathematics with good marks character:

communicative, self-confident and innovative working practice:

structured, goal-oriented and reliable experience and knowledge:
competent handling (2+ years) of programming experience preferably in python, some programming experience in with deep learning frameworks (pytorch, keras, or tensorflow), some familiarity with linux as well as ideally practical experience in deep learning algorithms and principles enthusiasm:

profound interest in deep learning or machine learning research passion of shaping future autonomous driving and making our deep learning detection algorithms more robust and safe language:

fluent in english responsibilities:

help shape the future:
the goal of your internship is to evaluate the robustness of classical and hybrid deep learning based approaches for computer vision from application areas such as autonomous driving. create something new:

you develop and deploy novel algorithms in the field of robustness/adversarial deep learning for computer vision. furthermore you implement and evaluate them using public data or bosch data. experience cooperation:

you conduct technical discussions and create new ideas within the existing deep learning research team. take responsibility:

besides you are responsible of the clean, efficient and well-documented implementation of new deep learning methods. networked communication:
optionally you publish a research paper in a tier-one ai conference.

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