Dr. Torben Hodl-Kuehnast Scientific evidence workflows

Bioanalytics | Microbiology | Scientific data | R&D project delivery

Dr. Torben Hodl-Kuehnast

PhD life scientist with industry-relevant experience across bioanalytics, microbiology, scientific data, and R&D project delivery.

I connect experimental biology, assay development, data analysis, documentation, and coordination into reliable outputs for life-science teams.

Hands-on in molecular microbiology, qPCR/RNA workflows, biofilm assays, SOP-driven diagnostics, omics/Python/R analysis, and project ownership up to EUR 500k.

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Bioanalytics and assay logic qPCR, RNA/DNA workflows, biofilm assays, cell-based readouts, microscopy, controls, and troubleshooting.
Quality-aware execution SOP-driven BSL-2 diagnostics, sample handling, documentation habits, and validation-oriented thinking.
Scientific data fluency 16S, WGS, NMR metabolomics, Python/R analysis, biological interpretation, and clear reporting.
R&D project management Budgets up to EUR 500k, external providers, timelines, workstream alignment, risks, and stakeholder updates.
AI-native review practice Structured extraction, evidence classification, validation gates, traceable outputs, and human review.

Industry fit

One profile, several industry entry points.

1

From biological readouts to robust assay evidence

For bioanalytics, molecular characterization, assay development, and lab-facing R&D roles: I bring controls-first thinking, troubleshooting, and method logic grounded in real wet-lab work.

  • qPCR
  • biofilm assays
  • plate readouts
  • method development
  • validation mindset
2

From complex biology to decision-ready interpretation

For translational R&D, biomarker, QSP-adjacent, and specialist scientist roles: I connect mechanism, disease context, omics, and experimental constraints into a clear scientific argument.

  • microbiome
  • immunology
  • molecular biology
  • metabolomics
  • biomarker logic
3

From unstructured inputs to reviewable data products

For scientific data, digital, bioinformatics, and AI strategy contexts: I design structured workflows where AI helps extract, compare, classify, and report without hiding the evidence trail.

  • Python
  • R
  • 16S/WGS
  • structured outputs
  • AI-native workflows
4

From scientific workstreams to R&D project delivery

For R&D project management, project lead, lab leadership, and operations roles: I am useful where scientific judgment, timelines, provider coordination, risk-aware handoffs, and stakeholder communication need to sit together.

  • R&D project management
  • timelines
  • external providers
  • risk handoffs
  • status reporting
Positioning signal

Research depth translated into execution

The profile frames scientific depth as industry-useful work: assays, documentation, data interpretation, stakeholder handoffs, and quality-aware delivery.

Team value

A bridge between lab, data, and decisions

The strongest signal is the combination. I can understand the biology, structure the data problem, and communicate what the evidence supports.

Modern work signal

AI-native, but reviewable

AI is used as a structured work layer: extraction, comparison, classification, drafting, validation, and transparent human review.

Signature evidence workflow

The differentiator is a reviewable process, not another skill list.

AI-native evidence workflow

Context

Life-science teams increasingly face too much unstructured scientific text, data, requirements, and documentation for manual review alone.

Work

Built workflows with schema-constrained extraction, weighted criteria, evidence classification, validation gates, coverage reports, and traceable outputs.

Signal

Modern AI use with scientific judgment: faster review, clearer gaps, defensible claims, and no hidden leap from model output to decision.

SOP-driven diagnostic execution

Context

RNA virus diagnostics in a BSL-2 environment with throughput pressure, sample handling, and time-sensitive reporting.

Work

Performed RNA extraction, reverse transcription, qPCR and tmPCR while supporting daily workflow organization and protocol optimization.

Signal

Hands-on regulated-lab exposure, SOP discipline, molecular workflow reliability, and comfort with operational constraints.

McMurGut microbiome modeling

Context

Murine cancer cachexia work needed a stronger bridge between microbiome composition, metabolite shifts, and mechanistic interpretation.

Work

Integrated 16S, WGS, NMR metabolomics, in vivo models, metabolic modeling, Python/R analysis, and provider-generated data streams.

Signal

Built a murine-tailored model catalog reaching 91% taxonomic coverage and used it to support interpretable biological conclusions.

Biofilm assay and target validation work

Context

Biofilm-forming organisms required comparative phenotype work and better hypotheses for intervention points.

Work

Developed and optimized biofilm assays, microscopy-based characterization, molecular workflows, and protein analytics in translational microbiology.

Signal

Evidence for assay development, phenotype interpretation, and industry-linked execution with Origimm, now part of Sanofi.

R&D project ownership across scientific interfaces

Context

Complex research programs often depend on external providers, heterogeneous data streams, deadlines, budget decisions, and scientific tradeoffs.

Work

Managed project work with budgets up to EUR 500k, coordinated sequencing, NMR, animal-study, and data-analysis interfaces, and translated progress into stakeholder updates.

Signal

Relevant for R&D project management, project lead, product, clinical, and operations contexts where scientific judgment and delivery discipline need to sit together.

Working style

How the work feels inside a team.

AI-native practice

I use AI as a daily scientific work layer for extraction, drafting, validation, comparison, and reviewable automation.

Structured ambiguity

I turn unclear biological questions into testable workflows with assumptions, controls, and review points.

Documentation discipline

I prefer methods and outputs that another person can inspect, repeat, and extend without guesswork.

Data translation

I connect readouts, omics layers, and biological context into concise explanations that support decisions.

Interface ownership

I coordinate across labs, providers, data sources, and stakeholders without losing scientific detail.

Operating range

Direct experience first, transfer context handled carefully.

Directly evidenced

  • Molecular biology: PCR, qPCR, tmPCR, DNA/RNA extraction, reverse transcription
  • Assay development: biofilm assays, cell-based readouts, microscopy quantification, plate-reader workflows
  • Protein and microbiology work: SDS-PAGE, Western blot, protein purification, cell fractionation, anaerobic cultivation, MALDI-TOF
  • Cultivation and upstream-like lab operations: media preparation, inoculation, microbial and mammalian culture workflows, condition control, and troubleshooting
  • Bioinformatics and data: 16S, WGS, Qiime2, Kraken, Bracken, ATLAS, Python, R, reproducible reports
  • R&D project management: budgets up to EUR 500k, timelines, external providers, workstream coordination, risk-aware handoffs, and stakeholder reporting
  • Scientific execution: SOP workflows, BSL-2 diagnostics, protocol optimization, documentation, onboarding, and cross-functional communication

Relevant transfer contexts

  • Bioanalytical and method-validation environments where assay logic, controls, documentation, and data interpretation matter
  • GxP, GMP, GLP, QA/QC, or quality-sensitive R&D settings where the transferable value is careful documentation, traceability, and risk-aware handoffs
  • R&D project management and project lead roles where scientific workstreams, timelines, providers, status reporting, and decision preparation need domain-aware coordination
  • Process-development and upstream bioprocessing support where media preparation, inoculation, cultivation conditions, culture monitoring, and troubleshooting translate into industrial process vocabulary
  • Downstream processing and analytical support where purification logic, fractionation, protein analytics, sample preparation, and assay readouts are relevant, without claiming GMP downstream ownership
  • Process Analytical Technology (PAT) and advanced analytics contexts where experiment planning, reliable measurements, data interpretation, and method evaluation matter, without claiming spectroscopy or autosampling hardware ownership
  • Clinical, biomarker, or translational programs where complex scientific evidence needs to become understandable decisions
  • Digital and AI-native scientific operations where workflows must be structured, auditable, and useful to human reviewers
  • Internal scientific interface roles involving technical judgment, stakeholder coordination, and clear communication in technically complex settings

Background

Where the evidence was built

Project Lead / Postdoctoral Researcher | Medical University of Graz

Microbiome-immunology, cachexia, multi-omics, metabolic modeling, provider coordination, timelines, stakeholder reporting, and EUR 500k project ownership.

Lab Scientist - RNA/Virus Diagnostics | iMAH GmbH

qPCR-based diagnostics, BSL-2 workflow execution, sample handling, SOP-based work, protocol optimization, and quality-aware handoffs.

PhD Molecular Microbiology | University of Graz

Biofilms, host-microbe interactions, assay development, cultivation workflows, molecular workflows, and industry-linked translational microbiology.

Foundational scientific work

BSc, MSc, and doctoral theses.

A dedicated page collects the earlier scientific work behind the profile: extremophile biology, biofilm assay establishment, and doctoral work in molecular microbiology.

Open theses page