Postdoc in User-Oriented Health and Welfare Technologies

You will be engaged in the THCS project titled: UPSCALE - Unfolding the processes between user needs and health and welfare technology in socio-technical transition of health and care services. The Utrecht University team includes Professor Ajay Bailey, Professor Niki Frantzeskaki, and Dr S. Labib. You will collaborate with other European partners in the project.

UPSCALE examines the socio-technical transition processes to understand the role of HWT (Health and Welfare Technology) use in the health and care transformation and provide transition pathways on how to achieve user empowerment via HWT use. The socio-technical transition perspective must be distinguished from initiatives focusing on individual technologies, for its truly “transformative” nature – the ability of being capable of determining a radical change in the socio-technical system (to deep-scale) – that differs from “incremental” changes offering innovative solutions but not preparing for real change in the health and care systems.

UPSCALE uses a novel combination of scientific methods and integrated frameworks to advance state-of-the-art knowledge of determinants that influence the emergence, development and future of HWT use in health and care. These determinants show in the interplay between human actors and social, institutional and technological structures. UPSCALE applies this knowledge to provide policy recommendations to enhance a smooth socio-technical transition and HWT’s contribution towards building the health and care of the future, addressing challenges and advancing dimensions such as equity, effectiveness and accessibility of services.

The tasks include the following:

• committing to research excellence and research with impact that is a mission of Utrecht University;
• engaging in the wider research and scholarly activities of the UPSCALE research project and Utrecht University UPSCALE team;
• engaging in the dissemination of results of the UPSCALE project by producing high-quality academic papers as a lead author;
• leading and co-authoring high-quality papers with the PIs, colleagues of UPSCALE and multiple research groups (e.g., spatial planning, international development, urban geography) at the department to produce academic outputs of European and Global Interest;
• conducting individual and collaborative research complying with and following research ethical standards;
• carrying out administrative and management work associated with the research.

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

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.

PhD Position: Understanding Human Biases During AI-aided Systematic Reviewing

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 and, hopefully, better 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 implications.

In the evolving landscape of AI-aided systematic tools (like ASReview), we need to explore the nuanced complexities and potential biases that arise when human discernment is coupled with the predictive capabilities of AI, aiming to foster a synergistic relationship that enhances the efficacy and reliability of the review process.

Therefore, in the subproject for which we are looking a PhD candidate, we will investigate questions like:

• Which biases - such as position bias or authority bias - do humans display when reviewing abstracts?
• How does knowledge about these biases generalize to other contexts, such as in doing research and university work generally.
• Should we replace human screeners with an AI (as predicted by OpenAI), or should we keep a human in the loop?
• How do (or should) humans interact with AI-aided screening models spotlighting potential biases inherent in AI-assisted processes?
• How can we reduce biases by humans and AI?

You will be responsible for conducting several high-quality experiments where humans interact with an AI to address such questions. Addressing these questions is crucial in fostering a collaborative synergy between humans and AI models, and to spotlight and reduce potential biases, fostering a more transparent and equitable research process. Moreover, it seeks to delineate the roles and responsibilities of human researchers in an AI-assisted landscape, paving the way for a harmonious and productive human-AI collaboration in academic research.

Universitair Docent Sociaal Recht

Als Universitair Docent binnen de afdeling Internationaal en Europees recht (onderdeel van departement Rechtsgeleerdheid) ben je actief in zowel onderzoek als onderwijs.

Je geeft onderwijs aan bachelor- en masterstudenten. De afdeling verzorgt onder andere de bachelorvakken Arbeidsrecht en International and European Labour Law en het masterprogramma Arbeidsrecht. In overleg met jou wordt bepaald welke vakken jij verzorgt. Je werkt in onderwijsteams waarin collega’s elkaar ondersteunen en van elkaar leren. Je werkt mee aan de verbetering van ons onderwijs waaronder: inhoudelijke ontwikkeling, didactiek, onderwijsvormen of beroepsvaardigheden. Je krijgt coördinerende taken in het onderwijs.

Je verricht voor minimaal 40% van je werktijd onderzoek op jouw vakgebied, dat aansluit bij één van de onderzoeksprogramma’s van het departement. Deze zijn ondergebracht in: het Montaigne Centrum voor Rechtsstaat en Rechtspleging, het Utrecht Centre for Regulation and Enforcement in Europe (RENFORCE), het Utrecht Centre for Accountability and Liability Law (UCALL), het Utrecht Centre for European Research into Family Law (UCERF) en het Utrecht Centre for Water, Oceans and Sustainability Law (UCWOSL).

Scientific research technician cancer stem cells (veterinary faculty)

Our group focuses on malignant of stem cells. Human embryonic stem cells and induced pluripotent stem cells can be maintained in culture indefinitely (self-renewal) and differentiate into virtually all cell types of the human body (pluripotency). Challenges in stem cell therapies include the detection and removal of incompletely differentiated cells, addressing the genomic and epigenetic alterations in the generated cells and overcoming the tumorigenicity of these cells that could arise on transplantation. Moreover, long-term culture of PSCs can lead to (epi)genetic drift, potentially activating processes that resemble malignant transformation as an adaptive mechanism to the culture conditions.

Indeed, the malignant potential of human pluripotent stem cells is not fully understood, and its evaluation currently relies solely on the assessment of the cells’ behaviour in vivo upon their engraftment into mouse models (teratoma assay). Our group is currently working on in vitro models able to answer the malignancy question. Stem cells are also key to normal development and health from conception through adulthood. Stem cells survive much longer than ordinary cells, increasing the chance that they might accumulate genetic mutations. It might take only a few mutations for one cell to lose control over its self-renewal and growth and become the source of cancer.

Our work has the aim to develop in vitro, animal-free models for detecting malignancy of stem cells and other relevant human and veterinary tumors.

Your daily work consists of supporting the team in hands-on practices such as cell and tissue culture, biochemistry, molecular biology, and histology.

Used techniques will include:

• cell culture of stem cells, including 3D cultures and 3D culture assays, organoids;
• microRNA analysis;
• single cell analysis including bioinformatic tools;
• generation of induced pluripotent stem cell lines using the CRISPR/Cas9 system;
• histology, advanced microscopy, FACS.