RNA-seq Experts in Germany
from over 15,000 CVs, matched in minutes with the power of AIHire experts who design sequencing workflows, process transcriptomic data and interpret differential expression, splicing or single-cell results. FRATCH matches you quickly with vetted, available freelance professionals whose RNA-seq skills fit your research or product needs.
Meet FRATCH Experts in Germany, who have recently used RNA-seq
Fatiha A.
Last position:
Science Coordinator at Heinrich-Heine-Universität
- Coordinate a large-scale, DFG-funded research project involving 19 interdisciplinary research groups across multiple institutions, including the MibiNext Graduate School
- Lead project governance and stakeholder communication through structured milestone tracking and reporting to university administration
- Organise and coordinate scientific events, including an international symposium and DFG on-site evaluation, managed invitations, participant communication and event-related enquiries
- Website maintenance and content creation
- Editing, formatting, reviewing, and compilation of abstract book and various documents
- Coordinate logistics, communication, scheduling, and stakeholder engagement across participating institutions
Nikolai G.
Last position:
Clinical Data Manager at Dr. Falk Pharma
- Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Kevin B.
Last position:
Procurator and AI Lead at ValueData GmbH
- Serve as AI lead for life-science solutions, integrating advanced AI models directly into company workflows and ensuring seamless deployment.
- Design and implement deep learning architectures (PyTorch, Keras) for complex biomedical challenges, including cell segmentation, multimodal omics analysis, and prediction of point clouds.
- Develop and deploy robust LLM-based systems, including RAG architectures and agentic workflows using LangGraph, to facilitate natural-language interaction with complex medical data.
- Lead cross-functional initiatives to apply foundation models and explainable AI (xAI) to clinical and evolutionary algorithms.
Ebenezer N.
Last position:
Applied Data Science & AI Bootcamp
- Prototyped LLM/RAG document assistant; trained transcriptomics and proteomic data; used Git/Docker for reproducibility.
- Strengthened ML fundamentals applicable to omics (feature engineering, validation, leakage control).
Nooshin O.
Last position:
Senior Computational Biologist at Max-Planck-Institute for Molecular Genetics
- Conducting research at the interface of proteomics and artificial intelligence, focusing on the application of machine learning models (e.g., neural networks, clustering algorithms, and feature extraction) to analyze complex biological datasets.
- Developing and teaching AI-based analytical workflows for molecular and proteomic data, integrating tools such as Python (scikit-learn, TensorFlow, Pandas) for predictive modeling and data visualization.
- Collaborating with interdisciplinary teams to explore data-driven hypotheses in molecular genetics and enhance biological interpretation through AI-assisted pattern recognition.
- Implementing automated data processing pipelines to improve reproducibility and FAIR data management in high-throughput experiments.
Mustafa K.
Last position:
Postdoctoral Researcher at National Institutes of Health (NIH), NHLBI
- Led peptide inhibitor design against UPF1 using RFdiffusion, ProteinMPNN, and AlphaFold2/3; validated candidates with molecular dynamics simulations
- Screened over 1 million compounds for ataxia fusion proteins via AutoDock Vina; confirmed top hits using GROMACS MD simulations
- Developed RNA-seq analysis pipelines for nonsense-mediated decay (NMD) studies including isoform quantification and motif discovery
- Modeled protein-RNA interactions using AlphaFold3 multimers integrated with BioGRID/STRING databases
- Established computational workflows for transcriptomics and RNA regulation in iPSC-derived human models
Kaan O.
Last position:
Research Associate at Uniklinik Münster
- Led a research project on investigating factors modulating HPV16 infection in keratinocytes by using microscopy and omics approaches.
- Curated and analysed RNAseq and mass-spec data and performed pathway analysis to identify target proteins.
- Responsible for the operation, proper maintenance, functionality and user training of microscopes and FACS equipment.
- Mentored master students on their thesis on how cell-ECM interactions play a role in HPV susceptibility in keratinocytes.
Muhammad U.
Last position:
Research Assistant at Saarland University
- Applied AI-driven CADD methodologies for biosynthetic pathway optimization and molecule screening.
- Integrated synthetic biology with computational chemistry workflows for rapid in-silico experimentation.
- Automated ML pipelines using Python, PyTorch, and Scikit-learn on Linux, improving model testing and reproducibility.
Pawandeep S.
Last position:
Research Technician at QIMA Monasterium GmbH
- Establishing flow cytometry experiments, including NGS workflow antibody panel design, to investigate how various drug treatments affect TNF receptor expression and influence activation and exhaustion of human-derived T cells.
- Conducting projects on the effects of various drug treatments on a human ex vivo wound healing skin model, performing PAS staining and quantifying the length of the epithelial tongue following injury.
- Investigating effects of drug treatments on hair follicles and ex vivo wound healing skin samples at the transcriptomic level by analyzing expression of specific target genes using quantitative PCR (qPCR).
- Managing daily ordering and inventory of laboratory consumables and equipment, following ISO 9001 standards.
Nhu Loc Thuy T.
Last position:
Ph.D. Researcher in Quantitative Genetics & Computational Biology at University of Cologne (CEPLAS – Cluster of Excellence in Plant Sciences)
- Generated and analysed large-scale RNA-seq data (>800 samples) using R, Python, and high-performance computing (HPC/Linux) systems.
- Integrated multi-omics data (genomic, transcriptomic, and phenotypic); applied Bayesian approaches and machine learning to study gene expression variation and inheritance of complex traits.
- Mentored B.Sc. and M.Sc. students in experimental design, programming in R/Python/Bash, biostatistics, data visualisation and scientific presentations.
Krupali P.
Last position:
Research Associate at Institute of Anatomy and Cell Biology
- Led single-cell and bulk RNA-seq analyses to identify rare airway and immune cell populations
- Designed and deployed multiple interactive R Shiny and Streamlit dashboards for real-time data exploration and communication with clinical partners
- Developed machine learning models for immunotherapy response prediction using single-cell transcriptomics and immune profiling data
- Built reproducible pipelines for single-cell and bulk RNA-seq analyses aligned with clinical and research standards
- Analyzed patient-derived datasets for biological marker identification using reproducible pipelines aligned with research and clinical standards
- Translated computational outputs into clear biological insights for mixed technical and non-technical audiences
Nitin P.
Last position:
Visiting Researcher at University of Strasbourg
- Computational and experimental analysis of jasmonate signaling pathways
Discover over 15,000 top freelancers
Statistics of experts using RNA-seq
Aggregated from the professional profiles of matched freelancers.
Experience
11 years
Position duration
2.3 years
Positions per freelancer
5
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Biotechnology, Education, Healthcare
Certification focus areas
Business Intelligence, Research and Development, Information Technology
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
83%
Certifications per freelancer
1
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Germany using RNA-seq
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
RNA-seq experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Biotechnology (100%)
- Education (67%)
- Healthcare (42%)
- Pharmaceutical (42%)
- Agriculture (33%)
- Banking and Finance (8%)
- Food and Beverage (8%)
- Information Technology (8%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What RNA-seq does
RNA sequencing, commonly called RNA-seq, measures RNA molecules in a biological sample to show which genes are active and how strongly they are expressed. It can reveal transcript abundance, alternative splicing, novel transcripts, fusion events and changes between experimental conditions. Unlike targeted assays, it can survey the transcriptome broadly and support discovery-led research.
From sample to insight
An RNA-seq project spans experimental design, sample preparation, library strategy, sequencing, quality control and computational analysis. Strong planning connects the biological question to read depth, strandedness, read layout, reference selection and replication. Professionals also document assumptions so that results remain traceable from raw reads to reported findings.
Ecosystem and tooling
RNA-seq specialists work across wet-lab and bioinformatics environments. Common tools and methods include:
- FASTQ quality control, adapter trimming and read alignment or pseudo-alignment
- Reference genomes, transcript annotations and gene-count matrices
- DESeq2, edgeR or limma for differential expression analysis
- STAR, Salmon, kallisto and featureCounts for transcript quantification
- R, Bioconductor, Python and workflow systems such as Nextflow or Snakemake
When companies need support
Companies bring in freelance RNA-seq expertise when internal teams need a complete analysis workflow, a faster route from sequencing data to findings or an independent review of existing results. This is common in biotechnology, pharmaceutical research, diagnostics, agriculture and academic life-science collaborations. In Germany, remote work often fits well, while on-site access may matter for sample logistics, laboratory coordination or regulated environments.
Signs of strong expertise
Look for professionals who distinguish biological replicates from technical variation, choose suitable statistical models and explain multiple-testing control clearly. They should connect quality metrics to study conclusions rather than treating pipeline output as proof. A reliable specialist can reproduce the workflow, manage metadata, version tools and present results for both scientific and non-specialist stakeholders.
Scope and collaboration
The right project scope depends on the organism, assay design, sample quality, sequencing provider and intended deliverable. A freelancer may provide a documented pipeline, processed count tables, differential expression results, pathway analysis, visualisations or a publication-ready methods section. Clear file conventions, secure data transfer and agreed review points support remote collaboration; German or English communication can be selected according to the project team.
Frequently asked questions
Curious about RNA-seq? Here are the answers that come up again and again.
RNA-seq is used to measure gene expression across a transcriptome and compare biological conditions. Companies use it to study disease mechanisms, treatment responses, biomarkers, cell states, transcript isoforms and changes in biological pathways.
RNA-seq can examine a broad range of transcripts and detect novel or unexpected RNA features, while microarrays depend on predefined probes. qPCR is highly useful for focused validation, but it usually measures a much smaller target set than an RNA-seq experiment.
A strong RNA-seq professional often combines experimental design with statistics, R or Python, Bioconductor, genome annotation and workflow automation. Knowledge of single-cell sequencing, spatial transcriptomics, cloud computing or laboratory quality processes can also be valuable, depending on the project.
The required experience depends on sample complexity, organism, study design and the expected deliverables. A focused differential expression workflow may need a different profile from a project involving de novo transcript discovery, single-cell data or regulated documentation.
Yes. RNA-seq analysis is often performed remotely when sequencing files, metadata and secure computing access are available. On-site collaboration can still help with sample handover, laboratory decisions and coordination with teams working in German research or life-science environments.
Ask a RNA-seq freelancer to explain read quality, alignment or quantification choices, replicate handling, normalization and false-discovery control. Review whether the workflow is reproducible and whether conclusions are supported by both statistical results and biological interpretation.
A RNA-seq specialist may deliver a reproducible workflow, quality-control report, count matrix, differential expression tables, pathway analysis and clear visualisations. The scope can also include cleaned metadata, documentation, handover sessions or support for a manuscript or internal research report.
RNA-seq includes approaches suited to single-cell and low-input work, but these require different library strategies, quality checks and analysis methods. A specialist should assess sample constraints early and select a workflow that matches the biological question, available material and sequencing design.
The average hourly rate of freelancers in Germany who have used RNA-seq in their recent projects is 78 €, which corresponds to a daily rate of about 621 € based on an 8-hour working day.
Of the freelancers in Germany who have used RNA-seq in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 83% hold a doctorate.
On average, freelancers in Germany who have used RNA-seq in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Germany who have used RNA-seq in their recent projects are German (100%), English (100%), and French (17%).
The most common industries among freelancers in Germany who have used RNA-seq in their recent projects are Biotechnology (100%), Education (67%), and Healthcare (42%).
The most common business areas among freelancers in Germany who have used RNA-seq in their recent projects are Research and Development (100%), Information Technology (50%), and Product Development (42%).
Main locations of FRATCH Experts, who have recently used RNA-seq
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
