Key Skills

Job Description

Help redefine cancer treatment through nanomedicine. We are seeking a highly motivated PhD candidate to work at the cutting edge of lipid nanoparticle design & molecular modeling Job Description SPARC (Scalable Personalized Nanotherapeutics for Cancer Treatment) is an ambitious research program dedicated to making advanced cancer therapies more effective and accessible, particularly for patients with hard-to-treat “cold tumors.” By developing personalized nanomedicines, SPARC aims to improve patient survival through innovations such as tumor-on-a-chip platforms that reduce reliance on animal testing, and novel manufacturing technologies that enhance therapeutic efficacy and product stability. As part of this program, we are looking for a highly motivated PhD candidate with a strong interest in interdisciplinary research to join a collaborative project between the Precision Therapeutics group (Dr. Alina Rwei) in the Department of Chemical Engineering and the Complex Fluid Processing group (Prof. Johan Padding) in the Department of Process & Energy at Delft University of Technology. The position will be formally embedded within the Precision Therapeutics group. In this PhD project, you will develop innovative formulation strategies for lipid-based drug delivery nanoparticles, with a particular focus on the design and optimization of externally triggerable lipid nanomedicines. Combining molecular modeling with experimental validation, you will investigate how formulation parameters influence nanoparticle stability, ultrasound triggerability, and biological performance, contributing to the development of precision therapeutic approaches for cancer treatment. The project is embedded in a strong academic–industrial network and offers close collaboration with leading pharmaceutical partners, including Merck KGaA and Johnson & Johnson. The project also involves close interaction with other research teams within the SPARC consortium, working on molecular mechanisms of nanoparticle purification and storage, as well as engineering solutions for downstream processing. This position offers a unique opportunity to work at the interface of nanomedicine, molecular modeling, and process engineering, while contributing to the future of personalized cancer therapy. Key Responsibilities Lipid-based nanoparticle formulation design & stability optimization Investigate the impact of lipid composition, cargo type and size, and solvent conditions on nanoparticle performance. Develop formulation strategies that balance: Mechanical robustness (required for purification and storage) Ultrasound responsiveness for triggered drug release Computational simulations with Coarse-Grained Molecular Dynamics (CGMD): Model formulation- and environment-dependent nanoparticle properties (e.g., bending stiffness, surface tension) Investigate ultrasound triggered release mechanisms Empirical validation of nanoparticle properties & ultrasound-triggerability in a biologically relevant environment Empirically validate the simulated nanoparticle properties Design and evaluate ultrasound triggerability of lipid-based nanoparticles that can activate the tumor microenvironment Perform in vitro ultrasound-triggered release studies under controlled conditions and validate performance in vitro e.g. tumor-on-chip platforms and in vivo Click here for the full job description and application procedure. Submission is possible until 31 August 2026

Core Responsibilities

The candidate will develop innovative lipid-based nanoparticle formulations and investigate their stability and ultrasound-triggered release mechanisms. This involves combining computational molecular modeling with experimental validation in biologically relevant environments like tumor-on-a-chip platforms.

Requirements

The role requires a highly motivated PhD candidate with a strong interest in interdisciplinary research at the interface of nanomedicine and process engineering. Candidates should possess expertise or strong interest in lipid nanoparticle design, molecular dynamics, and experimental validation.