[SocBiN] Two postdoc positions in Deep Learning and protein interactions

[Arne Elofsson]

Dear

Sorry for cross-posting.

Please spread the word that we have two postdoc positions in deep learning
on protein-protein interactions

*Postdoc at Stockholm University*
https://www.su.se/english/about-the-university/work-at-su/available-jobs?rmpage=job&rmjob=16725&rmlang=UK
*Postdoc at KTH*
https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:457741/where:4/




*Project description*Protein structure is essential for understanding their
function as well as for developing drugs targeting proteins. Recently, a
deep learning method that can predict the structure of most proteins was
made freely available and a database with predicted structure was released.
However, proteins do not act alone – they act together with other proteins.
Therefore, the next major challenge is to use these types of methods for
predicting protein-protein interactions. Initial studies from us have shown
that it is possible to predict accurate structures of a large part of
dimeric proteins using either a modified version of AlphaFold2 or
AlphaFold-multimer. However, there are still many proteins that cannot be
built accurately, nor are we able to always distinguish interacting from
non-interacting protein pairs and to build larger complexes accurately is
still an unsolved problem. In this project, we are recruiting two postdocs
to leverage recent advances in the field of machine learning to build
better deep-learning models for predicting protein-protein interactions and
to apply these methods to biologically relevant problems.

*DDLS:* The SciLifeLab and Wallenberg National Program for Data-Driven Life
Science (DDLS) is a 12-year initiative that focuses on data-driven
research, within fields essential for improving people´s lives, detecting
and treating diseases, protecting biodiversity and creating sustainability.
The programme will train the next generation of life scientists and create
a strong computational and data science base. The program aims to
strengthen national collaborations between universities, bridge the
research communities of life sciences and data sciences, and create
partnerships with industry, healthcare and other national and international
actors. Read more at: www.scilifelab.se/data-driven.

*Environment:* The Elofsson group is located at the Science for Life
Laboratory. Elofsson has worked on protein structure predictions for more
than two decades. He has worked on various techniques, both using machine
learning and other computational techniques. His most important
contributions for this work are the methods he has developed to identify
the quality of protein models, Pcons and various versions of ProQ. The
group consists currently of 5 PhD students and one senior researcher.
Azizpour’s group is part of the KTH division of Robotics, Perception and
Learning. He has extensive experience in computer vision and deep learning.
The main research directions pursued in Azizpour’s group have direct
relevance to this project which includes robustness and estimation of
uncertainty, transfer learning including knowledge distillation techniques,
non-standard deep networks e.g., graph networks and transformers, and
interpretable deep learning. Furthermore, the group has extensive
experience in deploying large experiments in GPU clusters. It consists of 4
PhD students, 1 postdoc, and several master students/interns.

*Resources:* The groups have access to the Berzelius computer (funded by
KAW). Berzelius is an NVIDIA® SuperPOD consisting of 60 NVIDIA® DGX-A100
compute nodes supplied by Atos. Each DGX-A100 node is equipped with 8
NVIDIA® A100 Tensor Core GPUs, 2 AMD Epyc™ 7742 CPUs, 1 TB RAM and 15 TB of
local NVMe SSD storage. The A100 GPUs have 40 GB on-board HBM2 VRAM.

Selected references:

   - Bryant, P, Pozzati, G. & Elofsson, A. “Improved prediction of
   protein-protein interactions using AlphaFold2” bioRxiv 2021.09.15.460468
   (2021) doi:10.1101/2021.09.15.460468.
   - David F. Burke, Patrick Bryant, ....Arne Elofsson “Towards a
   structurally resolved human protein interaction network” bioRxiv
   2021.11.08.467664; doi: https://doi.org/10.1101/2021.11.08.467664
   - Mehmet Akdel, .... Arne Elofsson, Tristan I Croll, Pedro Beltrao “ A
   structural biology community assessment of AlphaFold 2 applications”
   bioRxiv 2021.09.26.461876; doi: https://doi.org/10.1101/2021.09.26.461876
   - Federico Baldassarre, David Menéndez Hurtado, Arne Elofsson, Hossein
   Azizpour“ GraphQA: protein model quality assessment using graph
   convolutional networks“ Bioinformatics, Volume 37, Issue 3, 1 February
   2021, Pages 360–366, https://doi.org/10.1093/bioinformatics/btaa714
   - Erik Englesson, Hossein Azizpour, “Efficient Evaluation-Time
   Uncertainty Estimation by Improved Distillation”, ICML 2019 Uncertainty in
   Deep Learning workshop

This is a recruitment which is part of a joint grant for two postdocs this
one at Stockholm University financed by DDLS and one at KTH financed by
WASP.


Yours

Arne

-----------------------------------------
  Arne Elofsson                  Science for Life Laboratory
  Tel:+46-(0)70 695 1045   Stockholm University
  http://bioinfo.se/               Box 1031,
  Email: arne at bioinfo.se   17121 Solna, Sweden
  Twitter:   https://twitter.com/arneelof
  Zoom: https://stockholmuniversity.zoom.us/my/arneelof/
  Scholar: http://scholar.google.se/citations?user=s3OCM3AAAAAJ
  ORCID: 0000-0002-7115-9751 <https://orcid.org/0000-0002-7115-9751>
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