60.00 % (full-time doctorate)
The reproducibility of scientific findings is crucial for the credibility of empirical research. The objective of the Center for Reproducible Science is to train the next generation of researchers in good research practices, to develop novel methodology related to reproducibility and replicability, and to improve the quality of scientific investigation using meta-science.
The CRS brings together methodologists from across University of Zurich (UZH), working in fields which typically do not communicate with each other intensively. This methodological think tank allows to overcome traditional barriers between fields, and aims to determine sound state-of-the-art solutions to scientific challenges.
As a result, UZH researchers who are invested in replication or reproducibility efforts can get together with the methodologists of the CRS either through training activities, direct collaboration, or simply via publications.
The Neuroscience Center Zurich (ZNZ) will be a close project partner. The ZNZ is a joint competence center of ETH Zurich and the University of Zurich creating synergies between its 1000 neuroscientists in research and education
Neuroscience research is among the single largest animal research fields, with around 1.3 million used each year in the EU alone. In Switzerland, more than a quarter of experimental animal used in recent years were in neuroscience, and many undergoing experimental procedures of high severity. Despite the use of these numerous experimental animals, the overall success rate in therapy development for neurological diseases such as Alzheimer's dementia and stroke is low compared to other fields. Bridging this translational gap is critical to advancing both science and the 3Rs of replacement, reduction, and refinement. Although there are different reasons for this gap, weak or inappropriate design of preclinical studies has been flagged as key driver. For instance, the selection of animal disease models is often made based on the resources available to that researcher rather than which model provides the best representation of the specific pathophysiological process under study. Yet there is a lack of a comprehensive resource to assist preclinical neuroscientists make informed decisions on experimental parameters during study planning. This costs many experimental animals with only modest translational relevance. The advent of complex cell culture models, such as 3D organoids or microphysiological systems, has brought prospects to partly replace animal experiments for drug testing. However, no systematic curation of the application of such models has demonstrated their translational value in comparison with respective animal models.
Our ambition is to systematically curate the neurological-psychiatric therapy development pipeline by harnessing artificial intelligence and text analysis techniques. With this, our goal is to provide a comprehensive, up-to-date overview of translational success rates in neuroscience, including details of the experimental approaches used. This evidence-based resource will be made available as “living” online data warehouse, to guide researchers in designing their own animal studies.
Your primary responsibilities include the use of analytical, statistical, and programming skills to help collect, curate, analyze experimental data from scientific publications in the field of neuroscience, with a focus on text mining and natural language processing. In addition, creation of a user interface, e.g. with ShinyR.
Academic profile: Applicants with a master degree in data science, informatics, bioinformatics, or a similar field are encouraged to apply.
The two most important requirements are 1) genuine motivation for the project and 2) the willingness to show ones highest performance towards the goal of the project. In addition, we expect excellent written and spoken English skills. It is also helpful to have at least basic experience in text mining and/or natural language processing.
What we offer
We offer a purposeful work with the goal of improving animal welfare based on the implementation of 3R - reduction, replacement, and refinement of animal experiments.
One-to-one supervision by a junior group leader at the Center for Reproducible Science who has a medical degree and longstanding experience in both preclinical and clinical neuroscience (focus multiple sclerosis).
This project is a collaboration with Prof. Malcolm Macleod from the University of Edinburgh who is a world-leading expert in automated evidence synthesis (https://www.ed.ac.uk/clinical-brain-sciences/research/camarades). Sabbaticals at the University of Edinburgh during the PhD are highly encouraged.
Additional first- and/or co-authorships on peer-reviewed scientific journal articles will be encouraged wherever possible.
Place of work
Hirschengraben, 84, 8001 Zürich, Switzerland
Start of employment
Start date flexible, preferentially between December 2022 and February 2023.
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