Internship Data and Digital Enablement (PRLABD) (mfd)

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Internship in Machine Learning for Analytical Signal Data

Our Data and Digital Enablement department is part of Large Molecule Research (LMR) within Roches Pharma Research and Early Development (pRED) organisation in Penzberg. We develop implement and operate data-driven software and AI solutions for Process Research & Analytics including the processing and automated analysis of process and analytical data.

Your internship

You will develop and evaluate machine-learning and deep-learning methods for large datasets of analytical signals including time series spectra and multidimensional measurements. Your work will focus on information extraction representation learning and automated analysis ranging from benchmarking new approaches to developing robust usable ML tools.

You will take ownership of well-defined projects supported by your supervisor and scientists from different disciplines. You will also learn how the underlying measurements are generated and used in pharmaceutical research.

What matters to us

Teamwork: You enjoy exchanging ideas are open to feedback and contribute your own perspective. You value learning from colleagues while being willing to question established approaches.

Courage: You take ownership of your work and approach technical and scientific challenges with curiosity. You work independently and try new approaches when the obvious solution does not work.

Continuous learning: You are motivated to develop your skills work in a structured and persistent way and use setbacks to deepen your understanding and improve your approach.

Your profile

• This internship is open to students (m/f/d) enrolled at a higher education institution throughout the internship. Applicants in a gap year between their Bachelors and Masters degrees are also eligible with written university confirmation that enrollment will resume within the 12-month gap year.
• You are preferably at an advanced stage of your studies in a quantitative or computational discipline. Other academic backgrounds are welcome with relevant technical experience.
• You have hands-on experience implementing and evaluating ML approaches in Python whether through coursework research internships or personal projects. You are comfortable setting up your own development environment using Git and managing dependencies.
• Deep learning experience is a strong advantage. Additional experience with signal processing scientific computing software engineering Linux GPU computing or laboratory data is highly valued.
• Knowledge of drug development or biotechnology is a strong advantage. Prior exposure to life sciences or the pharmaceutical industry is a plus but not required.
• You are curious about the science behind the data take initiative and value collaboration and feedback. You communicate comfortably in English; German is a plus.

What we offer:

• Flexible working hours (37.5 hours/week)
• Attractive recognition grant (2268 Euro per month for a full-time internship > 3 months)
• Discounted meal prices (- 50%) in our staff canteen
• Fitness studio on campus
• Online/Offline student networking events
• The possibility of accommodation in the Roche Boarding House or convenient transport connections to Munich with the Roche shuttle
• Training opportunities and exciting guest lectures

Your application

Please submit your CV cover letter and current transcript of records.

In your cover letter briefly explain your motivation and describe one or two relevant technical or scientific projects highlighting your personal contribution. Please also share what you would like to learn during the internship. Links to projects code repositories publications or other relevant work are welcome.

A subsequent Masters thesis is possible depending on mutual fit the project and your universitys requirements.

Please remember that this internship is aimed at students (m/f/d) who are currently enrolled for the entire period of the internship or alternatively at people in a gap year of a maximum of one year (between Bachelors and Masters studies) where it can be proven in writing at which university/college and exactly when (month/year) within a year the studies will be continued.

Please note that recommendations for all student activities (e.g. internships theses etc.) are excluded.

Period: from February 2027 for a period of 6 months.

We are looking forward to your application!

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