PhD student (salary 54’000 USD/year)
Description of UZH unit:
Our lab develops novel methodological approaches to study variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. Specifically, we investigate the potential for plasticity, mechanisms for stabilization and compensation across the lifespan. For this, we acquire and analyze multimodal data sets, such as structural MRI, diffusion weighted data (DWI), simultaneous EEG and eye-tracking as well as behavioral data. From these rich data sets, we extract multivariate parameters and apply state-of-the-art methods, such as machine learning, functional network modelling, and longitudinal analyses.
The successful candidate will work on the Synapsis-foundation funded research project “Real-life activity tracking as pre-screening tool for early stages of Alzheimer disease”. The aim of the project is to investigate whether real-life activity measures, derived from wearable technology (e.g. GPS and accelerometer data), are sensitive to identify early stages of Alzheimer’s disease. Further, we aim to provide evidence that these real-life activity measures are associated with current AD biomarkers (i.e. high Amyloid level and brain atrophy).
The student will be expected to disseminate study results in peer reviewed journals, and to supervise Master’s students. The candidate will work in the team of Prof. Nicolas Langer, who is also part of the Neuroscience Center Zurich (ZNZ) (https://www.neuroscience.uzh.ch/en.html), which offers a renowned international PhD programme in Neuroscience. The candidate will work closely with the Institute for Regenerative Medicine (https://www.irem.uzh.ch/en.html), Geographic Information Systems (https://www.geo.uzh.ch/en/units/gis.html), University Research Priority Programme from the University of Zurich “Dynamics of Healthy Aging” (https://www.dynage.uzh.ch/en.html), and the Department of Computer Science at the ETH Zurich (https://www.systems.ethz.ch/).
80 – 100%
· MSc degree in a field related to cognitive neuroscience (e.g., cognitive neuroscience, (neuro-)psychology, computer science, biomedical or electrical engineering)
· Deep knowledge in data science (time-series data processing, feature engineering and analysis)
· Proficiency in programming (in Python, Matlab or R) is a must
· Knowledge in mobile and wearable digital technologies is desirable
· Experience with amyloid PET imaging and/or structural MRI analyses are a plus
· Training in machine learning is a plus
· Excellent verbal and written English skills
· To work in a team of highly motivated young researchers who are passionate about neuroscience, psychology and computer science
· A very competitive salary (54’000 USD/year) and generous social benefits
· Employment 3 years with the possibility of extension
· Generous support for professional travel and research needs (~3’000 USD/year)
· An inspiring work environment within the Department of Psychology and the University of Zurich and part of the Neuroscience Center Zurich (ZNZ) with many high-caliber collaborations (Department of Computer Science (ETH Zurich; Ce Zhang), Institute for Regenerative Medicine (UZH; Prof. Christoph Hock), Geographic Information Systems (UZH; Prof. Robert Weibel)
· The opportunity to live in Zurich, one of the world’s most attractive cities
Please visit https://www.pa.uzh.ch/en/Willkommen-an-der-UZH.html for further information.
This position opens on:
1.6.2022 (starting date)
Prof. Nicolas Langer, firstname.lastname@example.org
To be considered please stick to the following application format:
· CV including publication list and contact details of two referees (max. 3 pages)
· Statement describing motivations, personal qualifications and research interests (max. 2 pages)
· Save application in one single pdf file with the file name “Methlab_[SURNAME]_[name].pdf”
· Send application by email to: email@example.com
Applications will be considered until the position is filled (ideally submit your application before 31st of March 2022).
Description of the Project:
Real-life activity tracking as pre-screening tool for early stages of Alzheimer disease
Alzheimer’s disease (AD) accounts for the majority of all dementia cases and represents a major and rapidly growing burden to the healthcare and economical systems. The current state of research indicates that therapies need to be administered as early as possible. Therefore, there is an urgent need for accelerating biomarker discovery for early stages of AD. Importantly, evidences suggest that neurodegenerative changes precede clinical manifestations of AD by 20-30 years. However, prevailing potential biomarkers for early stages of AD diagnosis, including genetic testing, molecular examination of CSF, structural MRI and PET imaging, are highly limited as they can only be applied to relatively small sample sizes due to their excessive costs and invasive nature. This prevents their usage in large epidemiological studies; yet such study designs are imperative for identifying the intra-individual progression from healthy ageing to AD. Thus, novel non-invasive and inexpensive biomarkers are urgently required to be administered at large scale with the aim to identify individuals with indications for early stages of AD. These identified subjects could then be referred to undergo more cost-intensive examinations with the currently available biomarker techniques to achieve the desired diagnostic accuracy. Mobile and wearable digital technologies have an unprecedented potential and could close this current gap as they permit abundant, continuous longitudinal data acquisition at low costs to investigate intra-individual changes as early markers of AD. In this project, we will investigate whether activity measures, derived from GPS and accelerometer data, are sensitive to identify early stages of AD (i.e. mild neurocognitive disorder due to AD). Further, we aim to provide evidence that real-life activity measures are associated with current AD biomarkers (i.e. high Amyloid level and brain atrophy). Because our long-term vision is to develop a smartphone application that is able to identify intra-individual trajectories in healthy adults to establish the transition point to mild neurocognitive disorder due to AD as early as possible, we will examine the sensitivity of the real-life activity measures for intra-individual changes. Therefore, the present proposal serves as a proof-of-concept to address the prerequisites for real-life activity tracking as a pre-screening assessment tool to identify potential mild neurocognitive disorder patients.