Fall 2026

Working Student Intern

Control Team, Systems & Data Infrastructure

Posted on 7/21/2026

Phlair

Phlair

No salary listed

Edmonton, AB, Canada + 1 more

More locations: Munich, Germany

In Person

Onsite 5 days/week; relocation support to Munich.

Category
Data & Analytics (2)
,
Required Skills
Python
Grafana
InfluxDB
Postgres
Requirements
  • Education: Currently enrolled or completed studies in Computer Science, Mechatronics, Electrical Engineering, or a related technical field.
  • The 'Control' Toolkit: Proven experience with Databases and Grafana.
  • Understanding of IoT architectures and industrial protocols (OPC-UA / Beckhoff PLC experience is a massive plus).
  • Proficient in Python for data processing and automation.
  • High Ownership: You thrive in unstructured environments. You don't wait for a manual; you write it.
  • Attention to Detail: You understand that in hardware-software integration, the smallest configuration error can invalidate weeks of testing.
  • Commitment: Available on-site for at least 6 months.
  • Adaptability: You will get pulled in to side quests often when hands are needed, especially in mechanical build out.
  • Bridge the Gap: You possess the ability to translate complex scientific requirements into robust technical specifications. You listen to the needs of the lab and deliver solutions that solve their specific pain points.
Responsibilities
  • IoT & Data Infrastructure: Build and manage the pipeline capturing data from our Beckhoff PLCs via OPC-UA and other fieldbus technologies.
  • Database Mastery: Structure and maintain the databases (e.g., InfluxDB, PostgreSQL) that store our experimental data, ensuring it is clean and scalable.
  • Visualization & 'Deep Insight' Tools: Own our Grafana stack. Create intuitive dashboards that allow our scientists to compare different experiments in a highly visual and seamless fashion.
  • Full-Stack Automation: Develop internal tools and AI-enabled workflows (using Python or Claude Code) to automate the transition from raw experiment to reported insight.
  • System Reliability: Take responsibility for the health of our data flow from the sensor level to the cloud—no data gaps, no downtime.

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