[Opt-net] Post-doc / Engineering position in algorithmic optimization

[Yannick SPILL]

When determining the structure of a macromolecular complex, data from 
different sources can be used to aid reconstruction [1] . Continuous 
data, such as those collected in small-angle X-ray scattering or 
electron microscopy experiments, is a challenge to treat rigorously 
because of the correlations in the observed values. Recent work has 
shown that these data can be treated in a statistically rigorous way 
using gaussian process interpolation [2].

We have implemented such an interpolation procedure in the open-source 
Integrative Modelling Platform. It is written in C++, uses the 
high-level algebra library Eigen, and interfaces with python. Benchmarks 
have shown that the current implementation suffers from multiple 
algorithmic limitations that translate into a big computational burden 
for datasets larger than a thousand points.

The applicant must have excellent skills in object-based C++ programming 
and design patterns. He or she must be aware of the general caveats of 
matrix computations on floating-point units and their numeric stability. 
Basic linear algebra knowledge is also recommended, with a bonus for 
linear and nonlinear regression. Possible modifications of the code include

•Templating of all classes to avoid numerous virtual function calls

•Efficient use of sparse matrices

•Implementing various approximation algorithms for large datasets, such 
as Subset of Regressors, Subset of Datapoints or Projected Process [3]

•Parallelization of the code to multiple CPUs or GPUs

•Refactoring to make the code reusable for mathematically similar 
projects of the lab, such as the Self-Organizing Map, or its bayesian 
counterpart, Gaussian Process Latent Variable Model

Requests for information and applications should be addressed to Yannick 
Spill (yannick at pasteur.fr) and Michael Nilges (nilges at pasteur.fr). The 
project duration is at least one year, and will be funded by an ERC grant.



[1] Wolfgang Rieping, Michael Habeck, and Michael Nilges. Inferential 
structure determination. /Science/, 8:303–306, 2005.

[2] Yannick Spill, Seung Joong Kim, Dina Schneidman-Duhovny, Andrej 
Sali, and Michael Nilges. Bayesian treatment of continuous data for 
structure determination. In preparation, 2012.

[3] Carl Edward Rasmussen and Christopher K. I. Williams. /Gaussian 
Processes for Machine Learning/. The MIT Press, 2006.