Wednesday, January 28, 2015

Johns Hopkins ML Postdoc Position

I got this from my colleague Joshua Vogelstein:

The Open Connectome Project at Johns Hopkins University invites outstanding candidates to apply for a postdoctoral or assistant research scientist position in the area of statistical machine learning for big brain imaging data. Our workflow is tightly vertically integrated, ranging from raw data to theory to answering neuroscience questions and back again. Along the way, we develop new scalable methods (ideally with provable properties), and we apply previously developed methods in novel contexts. All of our projects include machine learning and big statistics, and integrate computer vision, systems engineering, numerical algorithms, and parallel computing. In short, we use/develop whatever technologies are necessary to answer today's most important, open, and long-standing questions in neuroscience.

The datasets that we work with are multi-modal, including multi-teravoxel images, high-dimensional spatiotemporal data, billion-vertex attributed graphs, 3D shapes, and semi-structured text. Therefore, we often focus on non-Euclidean and non-parametric methods. Publication targets include high-impact scientific journals, including Nature, Science, Nature Methods, and PNAS, with complementary articles in more specialized journals and conferences, including PAMI, NIPS, and Neuron.

Postdocs will primarily be advised by Dr. Joshua Vogelstein (Dept of Biomedical Engineering). In addition, postdocs will likely also be co-advised with at least one of Dr. Vogelstein's close collaborators, including Dr. Carey Priebe (Dept of Applied Mathematics & Statistics), Dr. Randal Burns (Dept of Computer Science), Dr. Guillermo Sapiro (Dept of Electrical and Computer Engineering, Duke University), and Dr. Michael Miller (Dept of Biomedical Engineering).

This position requires expertise in statistical machine learning and an interest in neuroscience. Other useful skills include computer vision, numerical algorithms, optimization theory, and convex analysis. Proficiency in some scientific programming language (e.g., R, Python, MATLAB) is also required. Experience with parallel computing and neuroscience are advantageous. All the research artifacts derived from this postdoc will be open source and open access. This means that pre-prints go on arxiv, code goes on github, and data goes on, typically prior to publication.

To be considered, please send an email including: (i) a curriculum vita, and (ii) the names and email addresses of three references.

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