feat(sdu): add SDU PhD enquiry, verify thesis claims from source

Explore SDU Center for Energy Informatics PhD, Theme 2 (Predictive
Maintenance and Asset Management of Smart Energy Networks), Odense DK,
deadline 2026-08-20. JD scraped live via Playwright.

Verified the master's thesis against the source PDF rather than the
stored title alone:
- Vibration-based condition monitoring of CNC machine tools; features
  are the dimensionless waveform, peak, pulse, margin and kurtosis
  indices.
- Implemented a hybrid of rule-based reasoning and a 7-10-3 ANN.
- Evaluation measured throughput and latency, not model accuracy:
  ~500 SPS pipeline against 72.9 kSPS sensors.
- Corrects an earlier note in the same session: PSO was surveyed only
  and never implemented. Recorded under thesis_limits alongside the
  absence of real operational data and accuracy figures.

Also records the ECTS relative grade (B, top 35%), the existence of
English-language transcripts, and the concrete Bosch sensor-data types
(defect management records, wafer inspection images, PCM electrical
parameters) under BS-2.

Adds the enquiry email sent to Prof. Bo Norregaard Jorgensen, which
asks whether an industry candidate is viable and whether non-academic
letters of recommendation are accepted.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

output/sdu_phd_energy_informatics/enquiry_email_joergensen.md

@@ -0,0 +1,73 @@
+# 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.
+
+---
+
+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