

• Investigated Aspergillus spore germination using molecular biology, microbial cultivation, and advanced microscopy.
• Developed automated data analysis pipelines in R and Python for quantifying spore dynamics from time-lapse imaging datasets.
• Mentored MSc/BSc students and contributed to interdisciplinary collaborations, resulting in publications and conference presentations.
• Oversaw microbial contamination analyses for food and environmental samples, including bacterial/ fungal identification.
• Supervised quality control, enforced compliance with SOPs, and trained lab personnel in diagnostic protocols.
• Maintained high accuracy standards in routine and incident-driven microbial testing.
• Conducted applied research on amino acid production by Corynebacterium glutamicum and bio-pigment synthesis by Monascus purpureus.
• Proposed and designed an innovative microbial fuel cell concept using electrogenic bacteria and cyanobacteria for simultaneous wastewater treatment and bioelectricity generation (biolamp prototype).
• Performed microbial cultivation, monitoring, and proposal development independently.
Scientific writing
Researcher in molecular microbiology with expertise in fungal physiology, microbial adaptation, and quantitative biological analysis. Skilled in microbial cultivation, molecular techniques, and developing automated imaging and data-analysis pipelines using R and Python. Earlier work in microbial biotechnology explored amino acid production, pigment biosynthesis, and microbial fuel cell concepts, giving me hands-on experience with applied microbiology and bioprocess-oriented thinking. Passionate about bridging experimental microbiology with computational approaches to engineer microorganisms, understand microbial interactions, and contribute to innovative biotechnological solutions.
R – advanced Data cleaning, wrangling (tidyverse) Statistical analysis Time-series analysis Visualization (ggplot2) Custom analysis pipelines for high-throughput imaging data
Python – intermediate Data analysis (pandas, numpy) Visualization (matplotlib, seaborn)
Basic machine learning Clustering Regression models Exploratory modeling (applied, not black-box)
Data Visualization & Reporting ggplot2 (R) – publication-quality figures Python-based visualization Dashboard-style visual summaries (conceptual practical) Scientific storytelling with data Figure preparation for peer-reviewed journals
Bioinformatics & Scientific Computing Image analysis pipelines (custom-built) Quantitative single-cell / spore morphology analysis Time-lapse microscopy data processing Statistical modeling of biological processes High-throughput data handling Reproducible research workflows
Specialized Scientific Software Time-lapse microscopy analysis tools Image segmentation & tracking tools Statistical software environments Custom R packages / scripts (eg, germination metrics workflows)
Fungal Biodiversity Course, Westerdijk Institute, Utrecht, NL
Microbiology
Heterogeneity
Data analysis and visualization
Quantitative biology
Science for societal impact