PhD Position in machine learning for structural molecular biology
Universitat Pompeu Fabra (http://www.upf.edu) and Prof. Gianni De Fabritiis, Icrea research professor (https://es.linkedin.com/in/gdefabritiis), are looking to recruit a young scientist for a PhD of the duration of approximatively 3 years on the topic between machine learning, machine intelligence, simulations and computational biology.
This project aims to develop large scale artificial neural networks for machine intelligence applied to structural and computational biology. The aim is to go substantially beyond the state-of-the-art, exploring unsupervised and reinforcement learning approaches.
We expect the candidate to participate in the development of new learning approaches derived from hierarchical temporal memories (HTM), deep learning, other brain-inspired learning algorithms, GPU and distributed computing. By working in this project, the researcher will have access to state of the art computational project like GPUGRID.net and large amount of molecular simulation data, which will be crucial for the development and validation of novel computational protocols. This project is expected to lead to discoveries that will be publishable in the highest impact scientific journals.
|Barcelona, Spain,[[http://www.prbb.org/||Barcelona Biomedical Research park]]. The laboratory is part of the Barcelona Biomedical Research Park which, with a privileged location on the shoreline of the Mediterranean sea, constitutes one of the most exciting interdisciplinary research centres in Southern Europe with more than 1000 scientists in the building alone.|
The candidate will preferably have a profile in computer science, mathematics, physics, and engineering, but other fields are also considered. We seek exceptional candidates with a passion for programming and computing, the capability to think out of the box, the ambition to work in very innovative projects and very good communication skills in English. Prior knowledge in neural information processing, deep learning frameworks, HTM is desirable, but the aim is to go beyond these tools during the course of the PhD not just to use them. Most of the code will be likely written in C/C++ and Python.
The candidate will be able to use [[http://www.gpugrid.net/]], one of the largest volunteer computing project worldwide with thousands of GPUs. The laboratory is very well equipped with access to a local GPU and CPU cluster, hundreds of TB of storage and experimental facilities.
The molecular modelling will be using state of the art software environments in molecular modelling and simulations ([[http://www.htmd.org]]) developed by the research group in collaboration with the spin-off company Acellera ([[www.acellera.com]]).
There will be ample opportunities to present his/her work at international meetings and conferences and to collaborate with other research groups in USA and Europe.
Funding and Eligibility:
Interested candidates should apply by sending a complete CV and a short letter of presentation indicating their interests together with the names and addresses of three referees to: Gianni De Fabritiis (gianni.defabritiis at upf.edu). The title of this offer must be clearly stated in any communication.
Deadline: POSITION FILLED
Some Relevant References:
- S. Doerr, I. Ariz-Extreme, M. Harvey, G. De Fabritiis, Unsupervised Feature Learning of Protein Folding, submitted (2015).
- N. Stanley, S. Esteban and G. De Fabritiis, Kinetic modulation of a disordered protein domain by phosphorylation, Nat. Commun. 5, 5272 (2014).
- S. Doerr and G. De Fabritiis, On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations, J. Chem. Theory Comput. 10 (5), pp 2064–2069(2014).
- D. George, J. Hawkins, Towards a Mathematical Theory of Cortical Micro-circuits, PLoS Comput Biol 5(10): e1000532. (2009), http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532
- Desaphy J1, Raimbaud E, Ducrot P, Rognan D., Encoding protein-ligand interaction patterns in fingerprints and graphs, J Chem Inf Model. 2013 Mar 25;53(3):623-37 (2013)
- G. E. Hinton, R. R. Salakhutdinov,Reducing the Dimensionality of Data with Neural Networks, Science 28 July 2006, Vol. 313 no. 5786 pp. 504-507.
- Olshausen BA, Field DJ , Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images, Nature, 381: 607-609 (1996).