Position

A fundamental challenge in developing new medicines for Alzheimer's Disease (AD) is understanding the causal drivers of the disease as well as biological manifestations of their progression. At Roche Neuroscience & Rare Diseases (NRD), vast, multi-modal human datasets are leveraged to solve these critical problems and identify the next generation of therapeutic targets as well as novel biomarkers.

As a RiSE student in the Biomarkers & Experimental Medicine group, the selected candidate will lead a cutting-edge computational project aimed at this core challenge in AD. The work will involve an unparalleled, large-scale human biobank dataset, combining 'omics (genomics, proteomics) and other deep phenotyping data. The project will apply state-of-the-art causal inference methods and advanced machine learning models to build a validated "causal map" of AD risk with the potential to directly inform Roche’s drug discovery pipeline.

Opportunity

• Employ causal inference methods (e.g., Mendelian Randomization) on large-scale proteomics data to distinguish causal drivers of AD from its downstream consequences.
  
• Develop and apply advanced machine learning models to identify biological signatures of resilience to AD in complex, multi-modal human health data.
  
• Integrate findings from causal and predictive models to build a validated 'causal map' with the potential to inform novel therapeutic target identification and biomarker candidates for AD.
  
• Ensure model robustness through rigorous validation techniques and elaborate biological insights by making strategic use of rich phenotyping.
  
• Prepare results for a first-author publication in a leading scientific journal and present findings to internal R&D stakeholders.
  

Qualifications

Applicants should be enrolled in a PhD or medical degree program and be curious, quantitative thinkers with a strong interest in applying sophisticated methods to solve critical challenges in neuroscience and human health. Moreover, the ideal candidate is/has:

• Currently pursuing a PhD in a quantitative field such as Applied Mathematics, Statistics, Biostatistics, Computational Biology, or Genetic Epidemiology with a focus on neuroscience and human health. (Graduated PhD students cannot be considered for the role. Furthermore, candidates must be enrolled at university for 50 % of the duration of their stay at Roche; the PhD thesis cannot be defended in the first half of the internship.)
  
• Excellent data analysis and programming skills (Python is a must; R is a plus) and a strong theoretical foundation in high-dimensional statistics.
  
• A passion for translating complex biomedical research questions into formal, solvable problem statements.
  
• A passion for understanding, developing, adapting, and applying statistical methods from first principles to novel, complex biological datasets.
  
• Biomedical subject matter expertise (e.g., in genomics, proteomics, neuroimaging, AD, or drug development) is highly desirable and will be a key differentiator between candidates with similar computational expertise.
  
• Experience in working with large-scale genomics datasets (e.g., UK Biobank) is desirable.
  
• A collaborative team player with excellent communication skills in English.
  

Additional information

• Preferred start date: January 2026 or upon availability.
  
• Application materials: CV, cover letter, and a letter from the academic supervisor supporting the application to the RiSE Program.
  
• Note: Due to regulations, non-EU/EFTA citizens must provide a certificate from their university stating that an industry internship is a mandatory part of the university training.

The RiSE Program offers the opportunity to lead innovative computational projects, harness large-scale biobank data, and shape the future of drug discovery using cutting-edge causal inference and machine learning.