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>
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Claude Opus 5
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"WebFetch(domain:careers.telenor.no)",
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"WebFetch(domain:arbeidsplassen.nav.no)",
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"WebFetch(domain:www.finn.no)",
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"WebFetch(domain:akerbp.com)"
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"WebFetch(domain:akerbp.com)",
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"Bash(git commit *)",
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"Bash(grep -io \"bosch[^\\\\\"]\\\\{0,220\\\\}\" resume_builder/canonical/claims.json)"
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]
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}
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}
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@@ -157,6 +157,7 @@ _Update this section when starting/finishing a JD._
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| Session | Status | Next Command |
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|---------|--------|-------------|
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| SDU Center for Energy Informatics, Odense DK — PhD, Theme 2 Predictive Maintenance & Asset Management of Smart Energy Networks (job 4159) | **EXPLORATORY 2026-08-02** (deadline 2026-08-20). Real JD scraped via Playwright. Strong genuine fit: thesis = vibration condition monitoring, RBR+ANN hybrid, throughput-vs-sensor-rate finding; Bosch fab sensor data; Swisscom data products as AI foundation. **Hard constraint: DKK 37,075/mo ≈ CHF 56k — far below the 180k bar; user accepts this knowingly as a PhD.** Real blocker is 2 letters of recommendation (13 yrs out of academia). Top-30% doc does NOT apply (numeric grading) — do NOT submit the ECTS-B/top-35% certificate against it. No publications. **Enquiry email to Prof. Bo Nørregaard Jørgensen SENT 2026-08-02** (draft in `output/sdu_phd_energy_informatics/enquiry_email_joergensen.md`); asked two questions — industry-candidate viability, and whether non-academic letters of recommendation are acceptable. | Await reply. Critical path is the 2 LORs, not the documents — chase referees regardless of reply. Build an **academic-style CV** (education-first, not the 2pp industry resume) when the user gives the go-ahead |
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| Aker BP ASA — Data Product Architect, AI-ready Data Products (FINN 469067315) | **SUBMITTED 2026-07-30** (ahead of the 2026-08-02 deadline; Adjacent, **Evidence Fit 79/100**, Document Quality 93/100, hard gate PASS, Channel Weak; Stavanger/Oslo/Trondheim, English working language, no Norwegian and no clearance required, EEA work rights). 2pp resume + 1pp CL, both validator PASS. Honest practitioner framing: builds governed data products *inside* Swisscom's Data Mesh; role *authors* an enterprise framework — real level stretch, never fabricate authorship. **Atlassian Compass closed the catalogue-tool gap** (74→79). Remaining gaps: framework authorship, no energy domain, AWS vs their Microsoft/Cognite stack, **zero verified metrics**. | Done — await response; optional follow-up email to Per Olav Marthinsen |
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| Google - Software Engineer III, Business Home, Zurich (req 93922217108087494) | **CLOSED - NOT PROCEEDING 2026-07-28** (submitted 2026-07-27; Core, Evidence Fit 89/100, Document Quality 94/100; no interview). Rapid early-screen disposition; known risks were Mid-level versus current Staff scope, data/platform versus product-SWE positioning, preferred algorithms/accessibility gaps, and a cold channel. | Done - cohort rejection recorded; do not treat this outcome alone as a document-quality failure |
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| Microsoft — Principal Forward Deployed Engineer, SWE (German Speaking), Zürich (req 200043897) | **SUBMITTED 2026-07-27** (84.2/100 Pass 2; finalized 2-page resume + 1-page cover letter). Strong native-German, Staff progression, production ownership and enterprise-data-readiness case; honest gaps remain in end-to-end LLM delivery, Azure AI and strategic-account FDE experience. | Done — await response |
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@@ -187,4 +188,6 @@ _See `config.md` for user-specific corrections. Add verified errors here as you
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| 2026-07-27 | **SW-1 was not solo.** Swisscom AWS migration was written as "sole technical lead" / "Led migration of legacy stack." Dennis was primary engineer for **his own domains'** pipelines and a contributor to the wider programme. | `experience_swisscom.md` (role line + all 3 bullet variants + overclaiming warning), `config.md` |
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| 2026-07-27 | **Security Champion is 2025/2026 only, and is a team role — not an award.** Source files claimed "3 consecutive years (2023/24–2025/26)." User has now corrected this twice. **Default is OMIT** unless the JD explicitly requires security/DevSecOps. | `experience_swisscom.md` SW-5, `achievement_reframing_guide.md`, `skills_taxonomy.md` (3 rows) |
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| 2026-07-29 | **Atlassian Compass is the metadata/lineage platform for Swisscom data products** (user-supplied). Previously the KB named no catalogue/lineage product at all, which read as a hard gap against data-governance JDs. Practitioner use only — never claim admin, rollout or ownership. Does NOT license claiming Purview/Collibra/Alation. | `claims.json` (SW-7 scope + skills entry), `experience_swisscom.md` SW-7 |
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| 2026-08-02 | **Master's thesis verified against the PDF.** Vibration-based condition monitoring of CNC machine tools; hybrid **rule-based reasoning + 7-10-3 ANN**; throughput/latency evaluation (~500 SPS pipeline vs 72.9 kSPS sensors). **PSO was surveyed but NOT implemented** — an earlier note in this session wrongly listed it as an applied method. No real operational data and no accuracy figures: it is a methods prototype, not a validated system. Also recorded: ECTS relative grade **B (top 35%)**, English-language transcripts exist. | `claims.json` EDU-MENG |
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| 2026-08-02 | **Bosch data types named.** Fab sensor/process data: defect-management records, wafer inspection images, PCM electrical parameters (user-confirmed). Previously the KB named no concrete sensor-data types for Bosch. | `claims.json` BS-2 |
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| 2026-07-27 | **Bullet density.** Fixed 1L/2L/3L and character-band rules made bullets uniform and encouraged page filling. | Replaced globally with natural-length, evidence-led bullets; character counts are diagnostic only. |
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@@ -0,0 +1,73 @@
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# Informal enquiry — SDU Center for Energy Informatics, PhD Theme 2
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**To:** bnj@mmmi.sdu.dk
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**Subject:** PhD Theme 2 (Predictive Maintenance) — enquiry from an industry data engineer
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Deadline: 20 Aug 2026. Verified against the thesis PDF on 2026-08-02 — see
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`claims.json` EDU-MENG for what may and may not be claimed.
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---
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Dear Professor Jørgensen,
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I am writing regarding the PhD positions in Energy Informatics (job ID 4159), specifically
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Theme 2 on predictive maintenance and asset management of smart energy networks. I come
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from industry rather than academia, and I would like to ask you directly whether that is a
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realistic profile for these positions before I invest in the full application.
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My master's thesis (M.Eng., Universität der Bundeswehr München, carried out at Tongji
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University Shanghai, graded 1.0) built a remote fault diagnosis system for CNC machine
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tools from vibration sensor data, using the dimensionless waveform, peak, pulse, margin and
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kurtosis indices as features. I deliberately combined two diagnosis methods: rule-based
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reasoning for interpretable physical thresholds, and a small feed-forward neural network
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for the noisy cases that static rules cannot separate — an early attempt at the
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interpretability trade-off that Theme 2 also raises. It was a methods prototype validated
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on benchmark and synthetic data, not on operational data, and its most useful finding was a
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negative one: the diagnosis pipeline sustained roughly 500 samples per second while the
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vibration sensors it was meant to serve delivered 72.9 kSPS. The model was never the
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bottleneck. The data path was.
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I then spent thirteen years building exactly that data path. At Bosch Semiconductor
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Manufacturing Dresden I worked on analytics for fab sensor and process data — defect
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management records, wafer inspection images, and electrical parameters from process control
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monitoring — including integrating containerised ML inference into a 24/7 production
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environment. I am currently a data engineer at Swisscom, where I build and own governed
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data products within the company-wide data mesh on AWS, together with the pipelines and the
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metadata and lineage behind them. That work is explicitly the foundation layer for the AI
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strategy the company intends to build on top, and the argument I make daily is much the
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same one Theme 2 makes for energy infrastructure: trustworthy, well-described operational
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data is the precondition for anything downstream.
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Theme 2 lists sensor measurements, operational data, inspection records and asset
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information as the sources to be combined. Integrating precisely those categories into
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something reliable enough for other people to make decisions on has been my job for over a
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decade — but always under delivery pressure, where the honest answer to "how well does this
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actually generalise" is usually "we did not have time to find out". A PhD is the deliberate
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move back into a setting where that question can be answered properly, and I would like to
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bring the operational side of the problem with me rather than leave it behind.
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On location: I am fully aware the positions are in Odense and I see the move as part of the
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attraction rather than a cost. I have lived and worked in Norway, I hold German citizenship
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and currently live in Switzerland, and returning to Scandinavia is something I would
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genuinely welcome.
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Two questions, if I may:
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1. Does the centre see a candidate coming from industry after this long as a realistic fit,
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or does the assessment process in practice favour applicants moving directly from a
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master's programme?
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2. The application asks for two letters of recommendation. My academic referees are
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thirteen years in the past. Would letters from current and former managers and
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colleagues be acceptable, or must at least one come from academia? I also hold German
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employment references (Arbeitszeugnisse), though I appreciate these are a national
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format that may not travel well.
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I would be glad to send my CV if that is useful.
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With kind regards,
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Dennis Thiessen, M.Eng.
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Bern, Switzerland
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dennis@thiessen.io · +41 795 955 585
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linkedin.com/in/dennis-thiessen
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@@ -32,7 +32,11 @@
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"end": "2013-10",
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"thesis_institution": "Tongji University, Shanghai",
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"thesis_title": "Development of a Web-Based Remote Fault Diagnosis System",
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"thesis_grade": "1.0"
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"thesis_grade": "1.0",
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"thesis_relative_grade": "ECTS B (top 35%); official certificate on file",
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"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.",
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"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.",
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"transcripts_language": "English-language originals available for B.Eng. and M.Eng."
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},
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{
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"id": "EDU-BENG",
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@@ -162,7 +166,7 @@
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},
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{
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"id": "BS-2",
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"scope": "Developed data services in Python, Java and C# over Oracle and Hadoop/Impala for internal analysis teams.",
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"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.",
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"allowed_verbs": ["developed", "built"],
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"metrics": "unverified"
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},
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