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The Position
Pharmaceutical & Solid State Development is part of Pharma Technical Development and situated in Basel, Switzerland.
Our key expertise is the development of robust and economic processes for drug substances with tailor-made solid state properties and patient centric formulations for the synthetic molecule portfolio of projects from early clinical studies to commercialization. We advance the design and delivery of transformative medicines by combining particle engineering, characterization, and formulation development - ensuring optimal drug performance, manufacturability, and patient-centric solutions from molecule to medicine.
As an intern you will support the department Pharmaceutical & Solid State Development in developing image-based machine learning workflows to characterize and classify spray-dried materials into distinct subpopulations.
The Opportunity
- Review of the relevant literature applicable to the project
- Training in relevant SOPs and manufacturing/analytical equipments
- Optimize and advance analytical characterization methods for spray-dried materials.
- Leverage machine learning to automate the classification and characterization of diverse particle shapes
- Regular discussion of project progress with supervisors and planning of next steps
- Summarize work in scientific reports and present results to the Roche Synthetic Molecule development community.
Who you are
You are a University student (or you graduated within 12 months prior to the start date) preferably in the subject of Pharmaceutical Sciences, Pharmaceutical Technology, Data Sciences, Cheminformatics or Chemistry and have an interest in technical and analytical issues. Furthermore, you are interested in using and sharing your knowledge as part of your Internship in a modern working environment. Your further profile for this position:
- Reliable and accurate working style in a non-GMP environment
- Knowledge of Python and core data science libraries (such as OpenCV, scikit-learn, TensorFlow, or PyTorch) to build image processing pipelines
- Self-sufficient, problem solving-oriented work habits
- Good knowledge of MS-Office (Word, Excel, PowerPoint) and AI tools
- Proficiency in English, German is a plus
This internship opportunity is one of a number of positions we offer in the department per semester and isnot suitable for writing a Bachelor/Master/Diploma thesis.
The interviews will take place on an interview day with all considered candidates and representatives of the department.
Your complete application includes the following documents:
- A Current CV and a Motivation Letter
- A certificate of enrollment (if you are currently studying)
- For Non-EU/EFTA citizens: Due to regulations, you must provide a certificate issued by your university stating that an Internship is mandatory for your studies and you must be enrolled during the entire duration of the internship
- The RiSM task (see below)
Application process
The application deadline is 23.08.2026. All successful final candidates will be invited to a virtual interview day which will take place on 14.10.2026.
The Internship will start in March to May 2027 and has a duration of 6 months, please indicate your preferred start date in your application.
More information about the Roche Internship program in Synthetic Molecules (RiSM) and other opportunities within this program you can find here.
Are you ready to apply? We are looking for someone who thinks beyond the job offered - someone who knows that this position can be a rare springboard to many other opportunities
Additional information (RiSM Task)
Assume you are a pharmaceutical data scientist tasked with identifying why different batches of a spray-dried amorphous solid dispersion (ASD) have a different compact/tableting performance, even though traditional laser diffraction analyses cannot discriminate between them.
What are your considerations with respect to setting up an image analysis pipeline, training a convolutional neural network (CNN) to generate a classification system, and correlating these findings with spray-drying process parameters to optimize drug product performance?
Please use the information provided in the following article (Hang Hu, Sampada Koranne, et al., "High-Speed Imaging-Based Particle Attribute Analysis of Spray-Dried Amorphous Solid Dispersions Using a Convolution Neural Network," Mol. Pharmaceutics 2025, 22, 488-497; https://doi.org/10.1021/acs.molpharmaceut.4c01092) to answer those questions.
We anticipate receiving your answers in the form of a slide deck suitable for presentation to a project team (maximum of 5 slides, PDF format) alongside your application.
Who we are
A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.
Let’s build a healthier future, together.
Roche is an Equal Opportunity Employer.