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Manufacturing & Log Data Analysis (m/f/x)
Topic: "Enable and facilitate root cause analysis in distributed manufacturing through log data classification"
01099 Dresden, Germany}', 5='{type=image, value=Image{width=800,height=428,url='https://www.systema.com/hubfs/Images/Offices/Dresden_Location.jpg',altText='',fileId=null}}', 6='{type=string, value=
With our headquarters in Dresden, we are strategically located in the largest center of microelectronic industries in Europe. As the capital of the German state of Saxony, Dresden is at the heart of “Silicon Saxony”, a term used to describe the region and its hundreds of microelectronics businesses.
Maintaining strong, collaborative relationships with local manufacturing companies and world-renowned universities (e.g. TU Dresden and HTW), Dresden is also the center of our R&D activities.
In addition to its high density of outstanding research institutions, Dresden is also well known for its history, beautiful baroque architecture, and broad collection of fine arts.
}', 21='{type=list, value=[{id=31544187646, name='MScha'}]}', 22='{type=number, value=1}', 24='{type=string, value=Unser Hauptsitz befindet sich in Dresden, der Hauptstadt des deutschen Bundeslandes Sachsen. Dresden ist das Herzstück von „Silicon Saxony“, Sachsens größtem Hightechnetzwerk und Europas größtem Mikroelektronikstandort.
Wir pflegen enge Kooperationsbeziehungen zu lokalen Fertigungsunternehmen und zu weltweit anerkannten Universitäten der Region (z. B. TU Dresden und HTW), wodurch Dresden das Zentrum unserer F&E-Aktivitäten ist.
Neben seiner hohen Dichte an herausragenden Forschungseinrichtungen ist Dresden auch für seine Geschichte, seine wunderschöne Barockarchitektur und seine große Sammlung an bildender Kunst bekannt.
}', 25='{type=string, value=Dresden (Deutschland)}', 26='{type=string, value=Dresden Headquarters}', 27='{type=string, value=Dresden (Deutschland)}', 28='{type=string, value=Germany}', 29='{type=string, value=Manfred-von-Ardenne-Ring 6}', 30='{type=string, value=01099}', 31='{type=string, value=Saxony}'}]
Deutsche Version verfügbar / German version available
In highly automated factories, log data from tools and services accumulate with rates of several GB per minute. Deriving insights from these data remains a challenge task. Multiple problems need to be addressed. Do my logs contain unusual messages?
How to enable structured log drilldown in distributed and highly dynamic manufacturing environments? How to avoid information overflow when interfacing with factoring staff through automated reporting?
In this internship or thesis project, you would evaluate and implement methods to structure log data based on log content similarities and structural time series modeling. This will enable the detection of abnormal events such as rare tool states, unusual material routes or stalled processes. Suitable methods shall be condensed and implemented into a process to simplify root cause analysis. In addition, abnormal log events and their factory-wide aggregation shall be used to design a global anomaly score intended to streamline and simplify alerting.
You should have programming experience or interest in Java, SQL, Azure & AI. Interest or knowledge in manufacturing processes would be helpful as well.