Machine Learning Research Internship
Pacmed / Amsterdam (NL)
As an intern at Pacmed Labs you directly contribute to Pacmed's mission: making healthcare more personal and precise with advanced machine learning and statistics. At Pacmed Labs we offer a research internship, where you will be responsible for advancing our knowledge on general medical AI problems and applying novel machine learning techniques. Examples are
- How can we better quantify uncertainty in predictions through the use of bayesian neural networks?
- What is the best way to model causal effects using observational data?
- How can we improve the interpretability of our machine learning models?
- How do we ensure that medical AI is fair?
We are looking for PhDs or exceptionally talented MSc students with strong technical and theoretical skills. Fluency in Python is required. You should be able to quickly up your game to match the knowledge level of our team of experienced researchers and data scientists, process complicated literature and set up experiments to validate your hypothesis.
This internship offers a unique opportunity to work on results that impact products with societal relevance. Furthermore, this research position will allow the intern to get industry experience while working on publishable material. The topic of the internship will typically be aligned with the research agenda of Pacmed Labs, but we are open for proposals from the applicant and open to discuss how to fit the internship in a PhD trajectory.
The ideal candidate for this position is:
- a (PhD) student in computer science, artificial intelligence, econometrics or applied mathematics
- experienced in applying advanced machine learning and/or advanced statistics to real-world problems
- fluent in Python and able to develop production-ready python code
- experienced in writing scientific publications
- driven by a societal purpose and excited about improving health care
- enthusiastic about working in a dynamic start-up environment
- currently living in the EU
- available for at least 4 months (full-time)