Department:

Department of Psychology at the University of Zurich (Switzerland), Methods of Plasticity Research (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be http://www.psychology.uzh.ch/en/chairs/plafor.html)

Position:

Postdoctoral Researcher (salary 98’000 USD/year; funding 2 years with the possibility of extension)

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 MR, 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.

Responsibilities:

The successful candidate will work on research in large scale multimodal projects to predict future cognitive decline from multimodal brain imaging data and non-brain risk factors (see project description below). Main responsibilities include the analysis of structural MRI data (optionally also DWI data), dissemination of study results in peer reviewed journals, and the supervision of PhD and Master students. The candidate will take part in ongoing projects and initiate new research within the team of Prof. Nicolas Langer, who is also part of the Neuroscience Center Zurich (ZNZ) (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be https://www.neuroscience.uzh.ch/en.html), closely collaborating with the University Research Priority Programme from the University of Zurich “Dynamics of Healthy Aging” (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be https://www.dynage.uzh.ch/en.html), the Healthy Brain Network of the Child Mind Institute (US), (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/) and the Department of Computer Science at the ETH Zurich (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be https://www.systems.ethz.ch/).

Workload %:

80 - 100%

Qualifications:

· PhD degree in a field related to cognitive neuroscience (e.g., cognitive neuroscience, (neuro-)psychology, computer science, biomedical or electrical engineering)

· Expertise in structural MRI analyses is a must

· Proficiency in programming in Python is a must (Matlab or R is a plus)

· Proficiency in machine learning (e.g. random forest regression models)

· Experience with DWI analysis is desirable

· Knowledge in resting state fMRI is a plus

· Excellent verbal and written English skills

· Interest in teaching about methods (e.g. applications of machine learning in neuroscience)

Language requirements:

English

We offer:

· To work in a team of highly motivated young researchers who are passionate about neuroscience, psychology and computer science

· A very competitive salary (98’000 USD/year) and generous social benefits

· Employment 2 years with the possibility of extension

· Generous support for professional travel and research needs (~5’000 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 (Frontlab, ICM, Paris, France; University of Montreal, Canada; University of Texas at Austin, USA; Neurospin, Saclay, France)

· The opportunity to live in Zurich, one of the world’s most attractive cities

Please visitMailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be https://www.pa.uzh.ch/en/Willkommen-an-der-UZH.html for further information.

This position opens on:

1.3.2021 (earliest, starting data is flexible)

More information:

Prof. Nicolas Langer, n.langer@psychologie.uzh.ch

Application

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: n.langer@psychologie.uzh

Applications will be considered until the position is filled (ideally submit your application before 31^st of January 2021).

Description of the Project:

Predicting future cognitive decline from non-brain risk factors and multimodal brain imaging data

The present project aims to develop a framework to predict individual future cognitive decline in healthy and pathological aging based on existing data from multimodal neuroimaging and non-brain risk factors like demographics, baseline cognitive state, health, health behaviors, and genetic (i.e. APOE) data. The project will use data from the Open Access Series of Imaging Studies (OASIS-3) database (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be oasis-brains.org) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be http://adni.loni.usc.edu/). This databases provides longitudinal data from multiple neuroimaging and risk factor data sources, allowing to characterize cognitive decline over multiple years in healthy and pathological aging in a large sample of over 1’000 participants. The multimodal neuroimaging and risk factors, from the baseline session will be used to predict cognitive decline at follow-up sessions via predictive models. The pilot experiment suggests that a combination of non-brain and structural data give the best predictions of future cognitive decline. The model needs to be refined by benchmarking different data processing strategies for input and further improving the generalizability by increasing the model’s robustness against site-effects.