# Informal enquiry — SDU Center for Energy Informatics, PhD Theme 2

**To:** bnj@mmmi.sdu.dk
**Subject:** PhD Theme 2 (Predictive Maintenance) — enquiry from an industry data engineer

Deadline: 20 Aug 2026. Verified against the thesis PDF on 2026-08-02 — see
`claims.json` EDU-MENG for what may and may not be claimed.

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Dear Professor Jørgensen,

I am writing regarding the PhD positions in Energy Informatics (job ID 4159), specifically
Theme 2 on predictive maintenance and asset management of smart energy networks. I come
from industry rather than academia, and I would like to ask you directly whether that is a
realistic profile for these positions before I invest in the full application.

My master's thesis (M.Eng., Universität der Bundeswehr München, carried out at Tongji
University Shanghai, graded 1.0) built a remote fault diagnosis system for CNC machine
tools from vibration sensor data, using the dimensionless waveform, peak, pulse, margin and
kurtosis indices as features. I deliberately combined two diagnosis methods: rule-based
reasoning for interpretable physical thresholds, and a small feed-forward neural network
for the noisy cases that static rules cannot separate — an early attempt at the
interpretability trade-off that Theme 2 also raises. It was a methods prototype validated
on benchmark and synthetic data, not on operational data, and its most useful finding was a
negative one: the diagnosis pipeline sustained roughly 500 samples per second while the
vibration sensors it was meant to serve delivered 72.9 kSPS. The model was never the
bottleneck. The data path was.

I then spent thirteen years building exactly that data path. At Bosch Semiconductor
Manufacturing Dresden I worked on analytics for fab sensor and process data — defect
management records, wafer inspection images, and electrical parameters from process control
monitoring — including integrating containerised ML inference into a 24/7 production
environment. I am currently a data engineer at Swisscom, where I build and own governed
data products within the company-wide data mesh on AWS, together with the pipelines and the
metadata and lineage behind them. That work is explicitly the foundation layer for the AI
strategy the company intends to build on top, and the argument I make daily is much the
same one Theme 2 makes for energy infrastructure: trustworthy, well-described operational
data is the precondition for anything downstream.

Theme 2 lists sensor measurements, operational data, inspection records and asset
information as the sources to be combined. Integrating precisely those categories into
something reliable enough for other people to make decisions on has been my job for over a
decade — but always under delivery pressure, where the honest answer to "how well does this
actually generalise" is usually "we did not have time to find out". A PhD is the deliberate
move back into a setting where that question can be answered properly, and I would like to
bring the operational side of the problem with me rather than leave it behind.

On location: I am fully aware the positions are in Odense and I see the move as part of the
attraction rather than a cost. I have lived and worked in Norway, I hold German citizenship
and currently live in Switzerland, and returning to Scandinavia is something I would
genuinely welcome.

Two questions, if I may:

1. Does the centre see a candidate coming from industry after this long as a realistic fit,
   or does the assessment process in practice favour applicants moving directly from a
   master's programme?

2. The application asks for two letters of recommendation. My academic referees are
   thirteen years in the past. Would letters from current and former managers and
   colleagues be acceptable, or must at least one come from academia? I also hold German
   employment references (Arbeitszeugnisse), though I appreciate these are a national
   format that may not travel well.

I would be glad to send my CV if that is useful.

With kind regards,

Dennis Thiessen, M.Eng.
Bern, Switzerland
dennis@thiessen.io · +41 795 955 585
linkedin.com/in/dennis-thiessen
