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In our lab we care about doing excellent science in a collaborative environment. Maintaining a healthy work-life balance helps us do the best, most creative scientific work we can in the short and long run. Moreover, we value team science – Lab members working collaboratively to share, enrich and improve each other’s expertise and experiences. Being a complete scientist also means thinking about your expertise in a broader perspective, which benefits careers inside as well as outside of traditional academic settings. For this reason, we support lab members exploring enriching experiences such as lab visits and rotations, international collaborations and internships at academic and non-academic settings. Finally, we value principles of diversity and inclusion – Everyone should feel welcome, regardless of their background, gender or sexual orientation or any other characteristic. Better science, together.



At the lab we are guided by the principles of open science. We make all our papers available as Open Access, and often post our work as preprints. Whenever possible in the context of secondary data analysis, we preregister our analysis plans to maximize transparancy (e.g, 123). We are research symbionts – We contribute to the scientific community by facilitating access to datasets, code and stimuli. For example, our work on the Cam-CAN project has already led to 600 groups from all over the world making use of our anonymized dataset, increasing the scientific value yielded from our study well beyond our own team. Additionally, we are part of international collaborations such as Lifebrain which bring together cohorts across Europe to maximize the scientific value and generalizability of our findings. At the same time, we benefit from amazing resources such as Biobank ABCDSHARE, and NKI-RS, as well as individual collaborations, to allow us to answer challening interdisciplinary questions using the best data available. In addition to papers and data, we endeavour to share stimuli (e.g. 12), slides, code (e.g. 12, 3 ) and data (e.g. 1, 23) available on resources such as Figshare, the Open Science framework, Github, and preprint servers such as BioRxiv and PsyArXiv.

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We regularly have visitors to the lab. Generally, we are interested in work related to developmental differences, changes and fluctuations in cognitive abilities such as reasoning, working memory and processing speed in childhood and old age. This includes work on the neural foundations of these abilities, external and internal influences such as the environment and increasingly using technology (e.g. tablets) to collect high temporal resolution data of cognitive change. We have a strong focus on statistical approaches to addressing such questions – For that reason, an interest in, or ideally experience with (statistical) programming languages such as R, Python and/or Matlab is essential, as is an interest in quantitative methodology such as SEM, linear mixed modeling, mixture modeling and related approaches.

Visiting students

We regularly have visiting students for periods of 6 weeks - 6 months. As we don’t collect small scale data, such visits would likely be centred on analysis of existing data within or beyond the lab rather than experimental work – Have a look at our papers to find out if there are topics that hold your interest. The earlier you reach out the easier it will be for us to help you find funding to support your visit.

PhD students

We occasionally have PhD vacancies, which will be advertised publicly. You are also encouraged to reach out to students within and beyond the lab to find out what the research environment at the Donders and the RadboudUMC is like.

Postdoctoral fellows and visiting scientists

It is possible to spend part or all of a fellowship with us. If you are interested, please reach out to Rogier. We have collated an overview of potential fellowships here. We also occasionally have ‘academic visitors’ – More senior scientists who spend time in the lab to share their work and work collaboratively on new problems.

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