Pilot Preventive Maintenance Machine Learning Internship

The Position

Internship for 6 Months Project :

Machine Learning / automated analysis of production data and anomaly detection for predictive maintenance applications

Task :

Roche operates a so-called data warehouse in which all production data is collected and analyzed to a certain degree. The data is obtained from hundreds of test fixtures that use various measurement techniques to ensure that the produced instruments and submodules adhere to their specifications.

Currently the webinterface of this data warehouse allows to display said data and the backend provides basic functionality that can automatically detect anomalies in the measurement results and trigger alarms. Following an alarm, a production engineer reviews the data and determines whether further action has to be taken or whether the alarm can be ignored.

The tasks for this internship are :

Understand the existing methodology and interface to the data warehouse

Expand the existing algorithms such that predictions can be made when specification limits are going to be exceeded. Include this information in the automated alarms

Evaluate the possibility of distinction between anomalies that require maintenance activities and anomalies that hint towards problems with devices under test

Implement a machine learning algorithm that takes the feedback of production engineers and improves the anomaly detection

Suggest improvements to data collection and analysis, identify gaps in the current implementation

Requirements

Ability to program in one of the languages python, R, java or java script

Technical background familiar with measurement methods and processes

Knowledge of statistical data analysis methods

Fluent German (due to existing documentation) and English

Preferred :

Pre-existing knowledge in the fields predictive / preventive maintenance

Studies in engineering or science

Ability to work independently with little supervision

What you can expect

Modern infrastructure of highest ecological and ergonomic standards

Flexible working environment (in offices as well as home office)

Flat hierarchies where input is appreciated

Opportunity to operate in a vast and diverse network, to realize projects together and get insights in various areas

Working in a fast responsive core team with globally operating stakeholders

Your complete application includes the following documents

A Current CV and a Motivation Letter

A certificate of enrollment (if you are currently studying). If you want to use this opportunity to do a Master’s or Diploma thesis, please highlight this in your application

Due to regulations, non-EU / EFTA candidates must provide a certificate issued by their university stating that an Internship is mandatory for the studies, and be continuosly enrolled at the university