Bioinformatics Experts in Germany

• Development of high-performance ad distribution via auction
• Holistic (multi-campaign & multi-channel) advertisement placement optimization
• Algorithmic optimization for NP-Hard/NP-e
• Multiple Knapsack Problem with constraints
• Online estimation of parameters in stochastic environments

Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker

Developed and trained deep learning models for large biological datasets. Built data and training pipelines. Tutor for machine learning courses. Supervised research interns and bachelor theses.

• Database restructuring
• UiPath programming and setting up the automation infrastructure with connection to Zendesk + in-house ERP
• Implementation of 6 processes
• AI and cloud review for later expansion
• Process Mining Analyst with UiPath and Celonis

• Conduct data management and statistical analysis for clinical studies on behalf of CROs.
• Guest lecturer at Ivancity University, Paris, specializing in data anonymization techniques and statistical disclosure control.
• Provide scientific and medical writing services for pharmaceutical companies.
• Perform optical mapping data analysis and develop software tools with a focus on algorithm optimization and technical support.

• 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.

• AWS
• Genedata Profiler
• Data Lake
• APIs
• Rstudio
• GitLab
• Python
• R
• Data acquisition, integration, and simulation
• Multiplex immunofluorescence
• Copy-number variation calling
• HLA typing and loss-of-heterozygosity analysis
• RNA expression analysis

• 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).

• Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
• Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
• Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
• Utilized OBS and professional audio equipment to ensure high-quality video and audio content
• Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
• Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation

• 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.

• 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

• Supported partner centers in adopting AI and LLMs-based predictive models for small-molecule discovery and therapeutic response using their genomic databases, designing failure modes for AI, and reducing feature-engineering time by 25%.
• Performed Python analysis and improved domain motion coverage by 30% through integration of free energy data with conformational modeling, mapping heterogeneous protein states for accurate structure–function analysis.

• Management of quality management system documents
• Management of the change control process
• Assessment and classification of customer complaints
• Assessment and classification/rating of customer complaints
• Identification and assessment of nonconforming materials and deviations
• Management of planned/unplanned deviations, CAPAs, and root cause analysis

• Built a GPT-based synthetic data pipeline that reduced acquisition cost and turnaround time by more than half.
• Modeled audience behavior across underrepresented groups using prompt workflows and statistical validation.
• Evaluated data realism with clustering, regression, and divergence analysis.
• Delivered reproducible Python workflows to automate experimentation in an Agile environment.
• Translated analytical results into clear insights for content and strategy teams.
• Technologies and skills: Python, Generative AI, GPT, Machine Learning, exploratory data analysis, Agile, GitHub, cloud computing, hallucination analysis.

• 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.