Internship active learning in probabilistic machine learning

Bosch packaging technology/osgood industries, inc.

• Renningen

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 The Bosch Center for Artificial Intelligence (BCAI) was founded in early 2017 to deploy cutting-edge AI technologies across Bosch products and services creating solutions that are

"Invented for life. "
Data collection is crucial in Bosch applications but it is often time consuming and expensive. Active Learning is a strategy to maximise the information gain within an experiment by choosing the most informative new sample. Since prediction from data collection isinherently uncertain, our research work is based on probabilistic models. As an intern at BCAI, you will have an overview of probabilistic machine learning and more importantly, gain the experience of the latest

"State of the art"
approaches. You will be an integral part of our research development and build a frame and test environment for our research work

* Help Shape The Future

:

configure and design the probabilistic machine learning framework for our research development. * gain experience:
you learn how to conduct machine learning software development. in addition, you learn, implement and apply state-of-the-art machine learning techniques. qualifications * education:

master studies in the field of mathematics, computer science, engineering or similar * character:

communicative, motivated team player and fast-learner * working practice:

autonomous, results-oriented and responsible * experience and knowledge:
basic knowledge in machine learning, ideally knowing probabilistic modelling (e. g. bayesian inference, gaussian processes), confident in programming and experienced in writing

"clean code"

on languages like python (preferred), java, c++ or matlab * languages:

fluent in english additional information start:

according to prior agreement 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? hon sum alec yu (business department) +49 174 4071682 qualifications:

education:

master studies in the field of mathematics, computer science, engineering or similar character:

communicative, motivated team player and fast-learner working practice:

autonomous, results-oriented and responsible experience and knowledge:
basic knowledge in machine learning, ideally knowing probabilistic modelling (e. g. bayesian inference, gaussian processes), confident in programming and experienced in writing

"clean code"

on languages like python (preferred), java, c++ or matlab languages:

fluent in english responsibilities:
the bosch center for artificial intelligence (bcai) was founded in early 2017 to deploy cutting-edge ai technologies across bosch products and services creating solutions that are

"invented for life. "
data collection is crucial in bosch applications but it is often time consuming and expensive. active learning is a strategy to maximise the information gain within an experiment by choosing the most informative new sample. since prediction from data collection isinherently uncertain, our research work is based on probabilistic models. as an intern at bcai, you will have an overview of probabilistic machine learning and more importantly, gain the experience of the latest

"state of the art"
approaches. you will be an integral part of our research development and build a frame and test environment for our research work. help shape the future:

configure and design the probabilistic machine learning framework for our research development. gain experience:
you learn how to conduct machine learning software development. in addition, you learn, implement and apply state-of-the-art machine learning techniques.

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Internship active learning in probabilistic machine learning
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