The advent of automated production in nanomedicine opens transformative opportunities for advancing healthcare innovation. By enabling high-throughput synthesis, precise control over nanoparticle properties, and scalable manufacturing, automation accelerates the development of novel nanomedicines, bridging the gap from academic research to clinical applications. Nanomedicine, a field that integrates nanotechnology with healthcare, harnesses nanoparticles—nanostructures formed from diverse components that self-organize into functional units due to their chemical or physical properties—to address a wide range of diseases. A landmark achievement highlighting this potential is the rapid development of mRNA vaccines, such as those for COVID-19, which demonstrated the power of nanotechnology in delivering life-saving therapeutics.
Despite its promise, the translation of innovative nanomedicines from concepts to real-world products is often hindered by challenges in expertise and manufacturability. Nanoworx, established in 2024 as a subsidiary of Eindhoven University of Technology (TU/e), addresses this gap by offering unparalleled expertise in nanomedicine design and access to cutting-edge equipment. Operating as a Contract Research Organization (CRO), Nanoworx combines advanced design and prototyping, automated library production, and high-throughput screening services to support academia, startups, and pharmaceutical companies in expediting and refining the development of next-generation nanomedicines.
InformationWe are seeking an enthusiastic and motivated postdoctoral candidate to join our dynamic team at Nanoworx! As an AI and machine learning expert, you will play a key role in help advancing our research capabilities computational approaches to aid the development of nanomedicine. You will design and implement the software infrastructure that connects our automated experimental platforms into a closed-loop self-driving laboratory. This includes developing robust orchestration software to coordinate multiple laboratory instruments, handling asynchronous data acquisition, and building APIs for real-time communication between hardware and optimization algorithms. You will implement and extend active learning strategies — such as Bayesian optimization — to efficiently explore complex parameter spaces with minimal experimental effort. The goal is to close the loop between automated experiments and intelligent decision-making, accelerating the discovery of nanomedicine formulations. Experience with Python, software architecture, and familiarity with concepts like job scheduling, error handling, and data management in experimental settings is highly valued. You will interface closely with the chemical biology cluster under supervision of prof. Dr. Tom de Greef and Dr. Nadia Erkamp, as well as the Precision Medicine group, led by prof. Dr. Willem Mulder.
Responsibilities include:
- Conduct innovative research in the field of AI and machine learning in the field of nanomedicine.
- Collaborate with team members and academic groups to design and implement research projects.
- Analyze and interpret data, delivering clear and concise reports, and contribute to team discussions.
- Develop data interpretation methodologies for large datasets of nanomedicine formulations.
- Mentor and supervise junior researchers, (PhD) students, and interns.
- Communicate research findings through research papers, scientific journal publications, and conference presentations.