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We are excited to offer a Master Thesis opportunity within the Pharma Technical Operations Quality (PTQ) and Pharma Technical Operations Excellence (PTE) teams at Roche. This thesis project, supported by the AI, Data & ML Engineering Cluster, will focus on benchmarking various Multimodal Retrieval-Augmented Generation (RAG) strategies for different types of documents. The goal is to explore and assess different methods to enhance information retrieval and data management for quality-relevant documents, directly contributing to our quality assurance and operational excellence initiatives.
Thesis Topic
Master Thesis: “Benchmarking Multimodal Retrieval Strategies of Quality Relevant Documents”
The Master Thesis will involve developing a comprehensive evaluation of different RAG strategies, specifically tailored for handling diverse document types, including multimodal content such as text, images, and other media. You will benchmark these strategies, focusing on their effectiveness and efficiency in processing both structured and unstructured data. The goal is to provide insights and recommendations that enhance the implementation of multimodal RAG methodologies within Roche’s technical operations. This project is critical for advancing our use of AI technologies in maintaining and improving our compliance and operational processes.
Your Responsibilities
Collaborate with experts from the PTQ and PTE teams to gather requirements and insights relevant to RAG strategies.
Design and conduct experiments to benchmark various retrieval methods across different document types.
Work with state-of-the-art tools and frameworks, including vector stores, multimodal retrieval methods, and other relevant technologies, to implement and evaluate RAG approaches.
Analyze results to identify strengths, weaknesses, and areas for improvement in existing strategies.
Develop documentation and reports summarizing findings, methodologies, and recommendations for future implementation.
Required Qualifications
Educational Background: Currently pursuing a Master's degree in Computer Science, Data Science, or a related field with a strong emphasis on AI and Machine Learning.
Technical Skills: Proficiency with vector stores and various retrieval techniques; experience in handling and processing both unstructured data and multimodal content.
Knowledge of LLMs and Prompting Frameworks: Hands-on experience with frameworks such as LangChain, LlamaIndex, and Haystack.
NLP Fundamentals: Understanding of natural language processing concepts, including Named Entity Recognition (NER), Bag-of-Words (BoW) model, Term Frequency-Inverse Document Frequency (TF-IDF), and word embeddings like Word2Vec or GloVe.
Problem-Solving Skills: Strong analytical abilities, with a systematic approach to tackling complex problems and developing innovative solutions.
Platform Knowledge: Familiarity with cloud platforms like Azure AI and AWS Bedrock is advantageous.
Agent-Based Frameworks: Experience with agent-based frameworks such as LangGraph, AutoGen, and LlamaAgents is beneficial.
Fluent English and German skills are required for the position
Benefits of the Thesis
Gain hands-on experience in a leading global pharmaceutical company, working on cutting-edge AI and ML projects.
Collaborate with a diverse team of experts, gaining insights into both quality assurance and operational excellence within a major industrial setting.
Contribute directly to projects that impact Roche’s production processes and quality management systems.
Receive mentorship and guidance to enhance your technical and professional skills.
Develop a robust understanding of how AI, data engineering, and ML can be applied in real-world scenarios to improve operational efficiency and regulatory compliance.
Details
Start Date: Flexible, starting from November 2024 or upon availability
Duration: 8 months, with the possibility of an extension
Application Process
To apply, please submit your CV, a motivation letter, and your academic transcripts.
Shortlisted candidates will be invited for an interview to discuss the thesis scope and expectations in detail. Following the interview, candidates may be asked to submit a brief project proposal outlining their approach to the thesis topic. Final selections will be based on these proposals.
Due to regulatory requirements, non-EU/EFTA citizens without a C-permit must provide a document from their university confirming that this Master Thesis is a mandatory part of their studies.
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