PhD Student - Near-Data Processing for Big Data and Machine Learning (m/f/d)

Munich, Bavaria, Germany

Full Time Entry-level / Junior
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Posted 2 weeks ago

Huawei is a leading global information and communications technology (ICT) solutions provider. Through our constant dedication to customer-centric innovation and strong partnerships, we have established leading end-to-end capabilities and strengths across the carrier networks, enterprise, consumer, and cloud computing fields. Our products and solutions have been deployed in over 170 countries serving more than one third of the world’s population.

The size of our cloud platform is gaining momentum and it is already planet scale. Huawei Cloud is one of the largest and fastest-growing platforms in the world. It has strong presence with over 40 availability zones located across 4 continents and 23 geographical regions, covering locations such as Germany, Hong Kong, South Africa, or Brazil, among others.

With 18 sites across Europe and 1500 researchers, Huawei’s European Research Institute (ERI) oversees advanced technology research, architecture evolution, design, and strategic technical planning across our network of European R&D facilities. Huawei’s ERI includes the Munich Research Center (MRC), located in Munich, Germany.

For our fast growing Intelligent Cloud Technologies Laboratory, we are looking for a:

PhD Student – Near-Data Processing for Big Data and Machine Learning(m/f/d)


The ideal candidate should have a passion and strong interest for building and working with distributed systems. Prior hands-on experience with systems programming and Big Data or Machine Learning systems is a big plus.

Project background

Near-Data Processing (NDP) solutions are becoming increasingly popular in the cloud because they have the potential to significantly improve performance by greatly reducing the amount of data transmitted between compute and storage tiers. This goal is more important than ever in today’s cloud environment because today’s cloud heavily rely on disaggregated architectures, where the compute and the storage tiers are physically separated for efficiency and cost reasons. However, the prevailing trend is that more and more data will need to be stored and processed, putting increasing pressure on the network connecting the compute and storage tier. NDP solutions provide an elegant solution to this problem by allowing a part of the computation to be executed in the storage tier, potentially leading to orders of magnitude reduction in the amount of data sent to the compute tier. At the high level, this PhD thesis will look at exploiting NDP solutions to improve both the performance of popular Big Data and Machine Learning solutions as well as overall resource efficiency.

Responsibilities

  • Perform research and development exploiting the novel concept of Near-Data Processing (NDP) to improve state-of-the-art Big Data and Machine Learning solutions
  • Improve data processing and resource efficiency in Big Data and Machine Learning solutions, measure system performance and resource usage
  • Track the latest progress of industry and academia on object storage, compute-in-storage, and data processing technologies
  • Actively participate in academic conferences, and improve the overall influence of Huawei Cloud

Requirements

  • MSc in Computer Sciences or other related disciplines
  • Hands-on experience with systems programming
  • Should be comfortable writing code in C/C++ and Java
  • Some familiarity with performance analysis of distributed systems
  • Familiarity with big-data open source tools (e.g., Hadoop, Spark, Flink, Hive, and Presto) and/or distributed machine learning systems (e.g., PyTorch, TensorFlow, SparkML) is a plus. Prior hands-on experience with the internals of such systems is a big plus
  • Familiarity with cloud services (e.g., AWS EC2, EMR, and S3) is a plus
  • Research experience with some publication record in the area of interest is a plus
  • Fluent in written and spoken English


By applying to this position, you agree with our RECRUITMENT PRIVACY STATEMENT. You can read in full our recruitment privacy statement via the link below.

https://career.huawei.com/reccampportal/portal5/grcprivacy.html

What you can expect

  • A broad range of training programs and opportunities
  • Interaction with a team of 40+ industry experts in various aspects related to distributed systems, storage technologies, machine learning, virtualization, etc.
  • Access to state-of-the-art distributed systems
  • Close mentorship


If you are enthusiastic in shaping Huawei’s Munich Research Center together with a multicultural team of highly skilled Engineers and Researchers, feel free to contact us. Driving future technologies focused on customer experience is our main mission. Apply now!


Please send your application and CV (incl. cover letter and reference letters) in English.

Job tags: AWS Big Data Distributed Systems Hadoop Java Machine Learning Open Source PyTorch R Research Spark SparkML TensorFlow
Job region(s): Europe
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