Research Intern: Knowledge Discovery and Pattern Mining (REMOTE or ON-SITE, FULL TIME or PART TIME)
**Job Family:** Research & Development
**Req ID:** 323448
We are looking for a Fall 2022 and Spring 2023 intern to contribute to our research activities by applying modern data mining, knowledge discovery, time series analysis, pattern mining, statistics, and machine learning techniques on a variety of structured (time series, tables, etc.) and semi-structured (logs, graphs, text) data sources in the field of Industrial Control Systems (ICS). As intern in this project, you will be responsible for developing new algorithms and prototype code as proof of concept for novel ideas in the field of advanced data exploration and analysis.
This task includes researching supervised and unsupervised methods and models for automatically finding non-linear patterns in the ICS domain with focus on security anomaly and intrusion detection. You will work with high-fidelity simulation and lab-emulated data to perform the research in close collaboration with domain experts in the field of OT security and data scientists. In addition, you will learn about general concepts and methods of intrusion detection in OT systems, understand security telemetry, data sources and the overall software architecture of data management systems in the ICS domain.
You will work in collaboration with a developer to prototype and implement selected models using data stored in a NoSQL database (Elastic stack) as part of a custom interface. This task will be jointly performed in collaboration with other team members including data scientists, security experts, and software developers. The goal of the project is to develop a user-friendly tool to explore and navigate in the data-rich domain of ICS. Successful candidates will have the opportunity to work in close collaboration with an outstanding development team focusing on a unique market approach for data exploration in ICS.
**The Challenge**
+ Research, design, and implement data-driven machine learning/data mining algorithms based on diverse data sources emerging in the ICS domain.
+ Dive into massive, noisy, and complex real-world systems
+ Explore the potential of big data for design, engineering and analysis tasks and support the development of revolutionary approaches
+ Present and discuss results (with potential of publication of results)
**Required Knowledge/Skills, Education, and Experience**
+ A current student pursuing a MSc./PhD degree in Data Science, Statistics / Mathematics / Computer Science / Computer Engineering, or other related subject with strong focus on machine learning and data mining. Please note, you must still be enrolled as a student during the internship period to qualify for the position.
+ Experience in the field of Big Data Analytics and machine learning with strong proficiency in data mining
+ Solid knowledge in statistics and exploratory data analysis
+ Strong proficiency in Python (including common analytics libraries)
+ Software development proficiency to work independently in Python
+ Capability for and interest in quick prototyping
+ Strong collaboration skills and ability to thrive in a fast-paced environment
+ Hands-on experience in command-line and development in Linux
+ Experience with the Elastic Stack (Elastic, Logstash, Kibana) desired
+ Experience with Jira and Gitlab, or similar platforms
+ Excellent knowledge of English (spoken and written)
Successful candidate must be able to work with controlled technology in accordance with US Export Control Law. US Export Control laws and applicable regulations govern the distribution of strategically important technology, services and information to foreign nationals and foreign countries. Siemens may require candidates under consideration for employment opportunities to submit information regarding citizenship status to allow the organization to comply with specific US Export Control laws and regulations. Additional information on the US Export Control laws & regulations can be found on https://www.bis.doc.gov/index.php/policy-guidance/deemed-exports/deemed-...
**Organization:** Technology
**Company:** Siemens Corporation
**Experience Level:** Student (Not Yet Graduated)
**Full / Part time:** Full-time
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