Postdoc Position: Computational Modeling, human psychophysics and fMRI of Natural Scenes and Objects

Start date: as soon as possible (posting date: Oct 1, 2010)

Duration: 2 years
Vision Lab, Computer Science Dept., Stanford University

The Vision Lab at Stanford University (lead by Dr. Fei-Fei Li) is seeking highly motivated applicant who is interested in doing research investigating the computational principles and neural representations of real-world image understanding. This is a highly inter-disciplinary project jointly run by Dr. Fei-Fei Li at Stanford and Dr. Diane Beck at the Beckman Institute at University of Illinois, funded by the National Institute of Health. We use both behavioral experiments and multi-voxel pattern recognition analysis of fMRI data to explore the underlying neural mechanisms of natural scene and objects understanding.

The postdoc scholar will be expected to lead a series of experiments to investigate the neural mechanisms for scene and object perception. A particular focus will be to combine sophisticated machine learning and statistical tools for analyzing high-dimensional fMRI data.

A successful candidate should: 1) have a strong background in fMRI and neuroscience in general, 2) have a strong computational background, preferably a bachelor degree (or minors) in engineering, mathematical sciences, computer sciences or equivalent; 3) have excellent communication skill with a particular emphasis in writing.

Interested candidates may send a cover letter and a CV to Dr. Fei-Fei Li ([email protected]). At least two reference letters will be requested at a latter stage of the selection process. We prefer an immediate start date.

Postdoc Position: Computer Vision and Machine Learning

Start date: as soon as possible (posting date: Oct 1, 2010)

Duration: 1-2 years (possibly renewable)
Vision Lab, Computer Science Dept., Stanford University

Research topics:
- High-level visual recognition problems
- Machine learning

Qualifications:
- Computer vision and machine learning background
- Strong publication record in computer vision or machine learning conferences
- Good coding and software skills

Contact:
Please email Fei-Fei at [email protected] with CV and names of two reference letter writers.

Graduate Research Assistant (PhD candidate or non-PhD student seeking RAship only): Computational Modeling, human psychophysics and fMRI of Natural Scenes

Start date: as soon as possible (posting date: Oct 1, 2010)
Vision Lab, Computer Science Dept., Stanford University

The Vision Lab at Stanford University (lead by Dr. Fei-Fei Li) is seeking highly motivated applicant who is interested in doing research investigating the computational principles and neural representations of real-world image understanding. This is a highly inter-disciplinary project jointly run by Dr. Fei-Fei Li at Stanford and Dr. Diane Beck at the Beckman Institute at University of Illinois, funded by the National Institute of Health. We use both behavioral experiments and multi-voxel pattern recognition analysis of fMRI data to explore the underlying neural mechanisms of natural scene understanding.

The student will be primarily involved in a series of experiments to investigate the neural mechanisms for scene and object perception. A particular focus will be to combine sophisticated machine learning and statistical tools for analyzing high-dimensional fMRI data.

A successful candidate should: 1) have a strong computational background, preferably a bachelor degree (or minors) in engineering, mathematical sciences, computer sciences or equivalent; 2) be highly skilled in programming, and familiar with the Linux/Unix as well as Windows environments; 3) have aspirations in making contributions in fundamental research in computational and/or cognitive neuroscience (some background knowledge is a big plus); 4) have excellent communication skill with a particular emphasis in writing. For non-PhD candidate seeking RAship only, we prefer students with an aspiration to apply for medical or graduate school later.

Interested candidates may send a cover letter and a CV to Dr. Fei-Fei Li ([email protected]). At least two reference letters will be requested at a latter stage of the selection process. We prefer an immediate start date.