4 Sep 2020

LambdaZero – Exascale Search of Molecules: Multiple Job Openings

Science:

Graph Learning Fellowship

Responsibilities: 

  • Participate in brainstorms; Propose solutions to the current limitation of graph-learning methods on protein-protein and drug graphs; in particular the information bottleneck
  • Implement solution in PyTorch, and torch geometric
  • Collaborate with Will Hamilton, Jian Tang, Michael Bronstein and Yoshua Bengio and students

About you, preferred qualifications:

  • 1-2 years experience with pytorch
  • Experience with TorchGeometric or other graph learning library
  • PhD student or recent graduate
Apply

Semi-supervised Learning Fellowship

Responsibilities: 

  • Participate in brainstorms; Propose solutions to the current limitation of representation-learning methods for gene-expression data. Some of the important problems are hidden confounder variables
  • Implement solution in PyTorch
  • Collaborate with Will Hamilton, Jian Tang, Brooks Paige, Charlie Roberts, and Yoshua Bengio and students

About you, preferred qualifications:

  • 1-2 years experience with pytorch
  • Interest in proteins/biology is highly valued
  • Experience with Torch and implementation of VAE, GANs
  • Interest in causal models
  • PhD student or recent graduate
Apply

Bioinformatics Fellowship

Responsibilities: 

  • Participate in brainstorms; provide bioinformatics expertise to the discussion. Screen relevant datasets. Propose solutions to combine different data sources in a biological sensible way
  • Implement solution with python, pandas, numpy
  • Closely collaborate with Relation Therapeutics

About you, preferred qualifications:

  • 1-2 years experience with python and pandas
  • Experience with Gene expression data, drug synergy scores, Protein Interaction Graphs
  • Some basic understanding of Machine Learning is highly valued
  • PhD student or recent graduate
Apply

 

Reinforcement Learning Fellowship

Responsibilities:

  • Participate in brainstorms; Identify limitations of model-based RL algorithms when applied to discrete optimization problems and propose solutions
  • Learn/understand baseline methods in non-convex discrete optimization during the project
  • Learn/understand common graph neural networks (GNN)
  • Implement solutions in code
  • Collaborate with Doina Precup, Pierre-Luc Bacon, and Yoshua Bengio

About you, preferred qualifications:

  • Previous experience 1-2 years in RL
  • Familiarity with Ray-RlLib
  • PhD student or recent graduate
Apply

 

Uncertainty Learning Fellowship

Responsibilities:

  • Design/optimize techniques to acquire data from oracles of different fidelities; oracles estimate energy of molecules; good oracles could cost 7 orders of magnitude more compared to cheap oracles
  • Be willing to learn/understand biophysics & biological assays we perform
  • Participate in discussions propose and implement new ideas
  • Collaborate with Brooks Paige, Jose Miguel Hernandez-Lobato, Yoshua Bengio

About you: preferred qualifications

  • Familiarity with models of uncertainty IE: ensemble of models, Bayesian Ridge Regression, MC Dropout, etc.
  • Familiarity with acquisition functions Batch-BALD, UCT, Thompson Sampling, Max Value Entropy Search, etc.
  • PhD student or recent graduate
Apply

 

Mathematics Fellowship

Responsibilities

  • Support mathematics requirements across a wide range of LambdaZero projects. Sometimes it could be deriving the loss function; sometimes it could be understanding the gradient explosion and deriving the normalization coefficients; sometimes it could be about if a certain kind of MPNN could solve 1-WL test.
  • Collaborate with all professors and student on the project

About you, preferred qualifications:

  • Undergraduate degree in mathematics, statistics, or other relevant field.
  • Emphasis on academic excellence (solid GPA and/or published works)
  • Critical thinking ability that could allow us to switch between parts of the project and quickly understand the problem and clearly communicate the solution.
  • Ability to code examples in python/jupyter-notebook
  • PhD student or recent graduate
Apply

 

Engineering:

Distributed Computing Fellowship

Responsibilities

  • Develop and optimize computational speed/performance of large-scale ML models executed in parallel on multiple GPUs including: model-based RL algorithms, hyperparameter search algorithms, bayesian acquisition function on large datasets
  • Further develop our our automated hyperparameter search techniques
  • Document your code rigorously
  • Review pull requests 

About you, preferred qualifications:

  • Experience with multi-GPU models
  • Experience with Project Ray
  • Practical experience in Facebook/Deepmind/etc/AI startup 
Apply

 

Cloud Computing Fellowship

Responsibilities

  • Continue the development of our existing auto scaling AWS cluster API to support requests from RL algorithms running in real time on another server
  • Develop distributed S3 instance data storage API

About you, preferred qualifications:

  • Experience with AWS/Azure or other cloud environment 
Apply

 

Algorithms Fellowship

Responsibilities:

  • Implement CUDA kernels, in particular for graphs 
  • Solve algorithmic problems such as fast shortest path on graph, or implement policy-based diffusion on graphs

About you, preferred qualifications

  • Past contributions to C++ code to one of the graph libraries such as torch geometric or similar
Apply

 

Operations:

Business Development Management Fellowship

Responsibilities:

  • Identify partnership opportunities
  • Develop relationships with pharmaceutical companies. Attend scientific conferences and events. Prepare, modify according to feedback, and close collaboration agreements.  
  • Ability to sometimes work on weekends

Preferred Qualifications

  • An undergraduate/graduate degree from a top-tier university could be a plus

Apply

Apply

 

The initial duration of a fellowship is 1 semester or 4 months. We might be able to offer a limited number of 1-year extensions depending on success of the project and personal contributions. 

 

We maximize the value of our team as a whole rather than per individual. Therefore we consciously/actively seek for team additions from diverse backgrounds. 

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