PhD Position: Optimal Models for AI-Assisted Systematic Review (ASReview)

Do you want to equip the social sciences for the looming AI revolution in academic literature exploration by investigating the best AI model for ASReview? If so, apply for this PhD position! This position is part of the bigger project “Transparent and Reproducible AI-aided Systematic Reviewing for the Social Sciences (TRASS)”.

Your job
The rapidly evolving field of AI offers promising solutions to the literature screening challenge using machine learning models, like active learning, and, very recently, large language models (LLMs). However, many of these AI-driven solutions emerge from tech companies that publish new models at an unprecedented rate. The rapidity of advancements in the field of AI outpaces meticulous scientific evaluations, leaving many methods unrefined and unproven. The challenge is twofold: keeping pace with these relentless innovations while collaboratively forging a comprehensive understanding of their strengths and limitations.

In the evolving landscape of AI-aided systematic tools (like ASReview), we need to explore which AI model can best be used for which type of data. For instance, while the active learning model (ALM) can facilitate literature screening by presenting the most likely relevant record, the human still needs to make the final labeling decision. In contrast, an LLM can directly generate such labels with explanations, without human input. This project envisions a collaborative approach that leverages the strengths of both humans and different artificial intelligence solutions in systematic screening.

You will be responsible for carrying out several high-quality simulation studies combining ALM/LLMs and testing the performance on 500+ labeled datasets.

Requirements:

We are looking for an enthusiastic colleague who meets multiple of the following requirements:

• You have (completed or almost completed) a Research Master’s degree in AI, NLP, Statistics, Data Science, Computer Science, or a related field.
• You have the ambition to conduct excellent scientific research and are motivated to create an impact with your research.
• You have an affinity with trans-disciplinary research.
• You have an interest in systematic reviewing and AI-aided techniques to accelerate the screening process and you want to figure out how and why they work.
• You have an affinity with or strong interest in machine learning / AI / NLP.
• You have a demonstrable affinity with open science, open-code and/or open-source software.
• You have experience with GitHub, R, Python, Markdown, or Jupyter Notebooks.
• You have excellent verbal and written communication skills in English.
• You are social, well-organised, communicative, and collaborative.
• You are interested in contributing to high-quality teaching, including supervising students, teaching lectures in data science and statistics.

Salary Benefits:

We offer:

• a collaborative, enthusiastic, and dedicated team that guids you in your research, teaching, and other tasks;
• high-quality PhD education, being part of a research school, following courses, presenting at international conferences, etc.;
• a position for 4-4,5 years (depending on the amount of teaching);
• a working week of 38 hours and a gross monthly salary between €2,770 and €3,539 in the case of full-time employment (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU));
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.

In addition to the terms of employment laid down in the CAO NU, Utrecht University has a number of schemes and facilities of its own for employees. This includes schemes facilitating professional development, leave schemes and schemes for sports and cultural activities, as well as discounts on software and other IT products. We also offer access to additional employee benefits through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest in their growth. For more information, please visit Working at Utrecht University.

36 - 40 hours per week

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