PhD Position: Optimal Models for AI-Assisted Systematic Review
2 maanden geleden
FACULTY: Faculty of Social and Behavioural Sciences DEPARTMENT: Social Sciences HOURS PER WEEK: 36 to 40 APPLICATION DEADLINE: 1 June 2024
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)”.
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 ), 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.
Your qualities
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.
Our offer
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 laid down in the CAO NU, Utrecht University has a number of schemes and facilities of its own for employees. This includes schemes facilitating , leave schemes and schemes for , 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 .
About us
A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At , the various disciplines collaborate intensively towards major . Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Pathways to Sustainability. .
The is one of the leading faculties in Europe providing research and academic teaching in cultural anthropology, educational sciences, interdisciplinary social science, pedagogical sciences, psychology, and sociology. Almost 7,000 students are enrolled in a broad range of and programmes. The Faculty of Social and Behavioural Sciences has some 1,100 faculty and staff members, all providing their individual contribution to the training and education of young talent and to the research into and finding solutions for scientific and societal issues. The faculty is located at near the historical city centre of Utrecht.
More information
For more information, please contact at k.vankats@uu.nl.
Please note that international candidates that need a visa/work permit for the Netherlands require at least four months processing time after selection and acceptance. Our (ISD) can answer your questions about living in the Netherlands as . Finding appropriate housing in or near Utrecht is your own responsibility, but the ISD may be able to advise you therewith. In case of general questions about working and living in The Netherlands, please consult the .
Candidates for this vacancy will be recruited by Utrecht University.
Apply now
As Utrecht University, we want to be a for everyone. We value staff with diverse backgrounds, perspectives and identities, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute.
To apply, please send your curriculum vitae, including a letter of motivation, via .
The first round of interviews takes place in June. An assessment might be part of the procedure. Our preferred start date is 1 September 2024.
The application deadline is 1 June 2024.
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