[Announcements] Postdoctoral fellow at the University of California San Francisco

[Duygu Tosun-Turgut]

Center for Imaging of Neurodegenerative Diseases at the University of
California, San Francisco (http://cind.ucsf.edu/) is looking for a highly
qualified candidate in the field of Computer Vision, Pattern Recognition,
Machine Learning, and Image Analysis interested and possibly with some
experience on Biomedical Data Analysis.



*Primary tasks will be to analyze the spatiotemporal spread of pathology on
the brain networks.* The fellowship will take place in a rich institutional
environment with many opportunities for further career development and
collaboration. The Center for Imaging of Neurodegenerative Diseases
supports aging and neurodegenerative diseases research by providing an
interdisciplinary team of faculty mentors, educational opportunities
related to neuroscience as well as medical imaging informatics, and a
wealth of neuroimaging, clinical, cognitive, and biospecimen data for
developing novel research projects.



The Fellows will analyze multimodality brain magnetic resonance imaging
(MRI) and amyloid and tau positron emission tomography (PET) data from
three different studies: 1) the Alzheimer's Disease Neuroimaging Initiative
(ADNI), a multi-site longitudinal study of aging and Alzheimer's Disease,
2) two DOD funded grants studying effects of traumatic brain injury and
post-traumatic stress disorder on Alzheimer’s disease in Vietnam Veterans
with and without mild cognitive impairment (MCI) using ADNI, and 3) a
longitudinal study to identify imaging biomarkers, which are predictors of,
and altered by, psychotherapy treatment response for late life depression.



For this reason, previous multidisciplinary experience is an added value
which will be properly considered.



*Job requirements: *

1.     Completed Ph.D. in Computer Science, Biomedical Engineering, Applied
Mathematics, or a related field, with neuroimaging data analysis as the
primary topic of the dissertation.

2.     Knowledgeable within one or more of the following subjects:
probabilistic graphical models, topic models and Bayesian non-parametric,
relational learning, deep learning, RBMs, neural networks and
auto-encoders, sparse and dictionary learning, kernel methods and manifold
learning, graph-based learning and spectral analysis.

3.     Understanding of fundamental brain MRI data analysis problems,
including registration, tissue segmentation, noise removal, etc.

4.     Experience using major MRI data analysis software such as FSL, SPM,
FreeSurfer, BrainVisa, etc.

5.     Experience using major imaging statistics techniques on R or Matlab.

6.     Proven ability to clearly communicate novel research findings
through oral presentation and written publication.

7.     Evidence of top quality research on the above specified areas in the
form of published papers in top conferences/journals and/or.



*Preferred characteristics: *

1.     Experience with diffusion-weighted MRI data analysis, including
preprocessing, tensor estimation, tractography/connectome

2.     Experience with task-free functional MRI data analysis, including
preprocessing and intrinsic functional connectivity (seed-based or
component-based)

3.     Experience with PET brain scans

4.     Experience processing large-scale data sets: data management,
scripting, etc.



The scientist is expected to publish his/her research results in leading
international journals and conferences. She/he is also expected to
contribute to the set-up of new project proposals, participate in funding
activities, supervising PhD candidates and collaborate with scientists from
different disciplines.



Salary will be commensurate to qualification and experience and in line
with international standard.



Start date: As soon as possible.

Those with interest should send a CV to:

Duygu Tosun
Assistant Professor
Department of Radiology and Biomedical Imaging
University of California, San Francisco
* duygu.tosun at ucsf.edu <duygu.tosun at ucsf.edu> *
http://www.radiology.ucsf.edu/cind
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