Basel, Basel, Switzerland
- Research and development of amorphous solid dispersions (ASDs) for poorly soluble drug candidates - Application of machine learning approaches to support formulation screening, classification, and stability assessment - Use of physics-based and thermodynamic modeling (COSMO-RS) to describe molecular-level interactions - Integration of experimental data with data-driven and physical models to enable informed formulation decisions
-Implementation of machine learning models for the classification of solid substances based on Raman spectroscopy data -Development of a convenient platform to simplify the use and application of these models in daily workflows -Streamlining data acquisition by using SDKs and designing a graphical user interface for efficient use
Master Thesis: Comparison of different artificial intelligence models for characterization of dermal histopathological slides -Evaluation of different transformer-based machine learning models for the classification of histopathologic specimens of skin melanomas -Development and implementation of Python algorithms to improve the accuracy and automation of the classification process
- Research and development of liposomal formulations, covering production, characterization, and optimization of nanoscale delivery systems - In vitro assessment in glioblastoma cell models, including cytotoxicity testing and cellular uptake studies - Quantitative analysis of liposome–cell interactions using flow cytometry, supporting uptake profiling and optimization of formulation parameters
Bachelor Thesis: Establishing a Fully Automated Protein-Ligand Binding Assay using Time resolved Fluorescence -Development of a fully automated protein–ligand binding assay based on time‑resolved fluorescence for high‑throughput interaction analysis -Integration and programming of a Beckman Coulter liquid‑handling robot for automated sample preparation and assay execution -Programming and automation of PicoQuant fluorescence spectrometer for data acquisition