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>

resume_builder/canonical/claims.json

@@ -32,7 +32,11 @@
       "end": "2013-10",
       "thesis_institution": "Tongji University, Shanghai",
       "thesis_title": "Development of a Web-Based Remote Fault Diagnosis System",
-      "thesis_grade": "1.0"
+      "thesis_grade": "1.0",
+      "thesis_relative_grade": "ECTS B (top 35%); official certificate on file",
+      "thesis_methods": "Verified against the thesis PDF 2026-08-02. Domain: vibration-based condition monitoring of CNC machine tools (piezoelectric accelerometers on spindle, tool rest, lathe body). Features: speed, load and the dimensionless waveform, peak, pulse, margin and kurtosis indices. Surveyed CBR, PSO, RBR and ANN; SELECTED a hybrid of rule-based reasoning (interpretable thresholds) plus a 7-10-3 feed-forward ANN (faultstates green/yellow/red) to handle noisy data that static rules cannot classify. Extensible plug-in architecture for swappable diagnosis methods and data collectors. Java/GWT client-server on MySQL. Evaluation measured throughput and latency, NOT model accuracy: the pipeline sustained ~500 samples/s against tri-axial vibration sensors delivering 72.9 kSPS, so the thesis concluded data reduction, filtering or batch scheduling is required for real-time use.",
+      "thesis_limits": "PSO was SURVEYED ONLY and NOT used in the implementation -- never claim it as an applied method. No real operational data: training data came from a cited thesis, test data was modified plus a random generator. No model-accuracy figures exist. Back-propagation retraining was listed as future work, not implemented. Frame as a methods prototype/framework, never as a validated production predictive-maintenance system.",
+      "transcripts_language": "English-language originals available for B.Eng. and M.Eng."
     },
     {
       "id": "EDU-BENG",
@@ -162,7 +166,7 @@
     },
     {
       "id": "BS-2",
-      "scope": "Developed data services in Python, Java and C# over Oracle and Hadoop/Impala for internal analysis teams.",
+      "scope": "Developed data services in Python, Java and C# over Oracle and Hadoop/Impala for internal analysis teams. Data types worked on (user-confirmed 2026-08-02): semiconductor fab sensor and process data -- defect management records, wafer inspection images, and electrical parameters from Process Control Monitoring (PCM). Relevant as genuine industrial sensor/asset-data experience.",
       "allowed_verbs": ["developed", "built"],
       "metrics": "unverified"
     },