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UID:submissions.supercomputing.org_SC20_sess280_job139@linklings.com
SUMMARY:Postdoctoral Appointee – Machine Learning, Deep Learning, Computer
  Vision for Ecology
DESCRIPTION:Job Posting\n\nPostdoctoral Appointee – Machine Learning, Deep
  Learning, Computer Vision for Ecology\n\n\n\nPosition Description:\n\nThe
  mission of Argonne’s Environmental Science Division (EVS) is to provide p
 ioneering fundamental and multidisciplinary research and innovative analys
 es to solve global and local challenges, applying our core competency in p
 redictive environmental understanding. Argonne is home to a wide variety o
 f computing systems, including some of the most powerful high-performance 
 computers in the world. These systems provide computing power to advance u
 nderstanding in a broad range of disciplines. \n\nThe EVS division seeks a
  post-doctoral appointee to develop an AI-enabled technology for collectin
 g data on wildlife behavior around renewable energy facilities in support 
 of the U.S. Department of Energy’s Solar Energy Technology Office. We seek
  a data scientist, who is passionate about machine/deep learning (ML/DL) a
 lgorithm development using images and video footage, interested in working
  with AI-enabled edge-computing cameras, and values interacting with exper
 ts in diverse disciplines. The successful candidate will develop vision-ba
 sed ML/DL algorithms/models – which includes training data collection, ML/
 DL model training and testing, supporting software development, and hardwa
 re-software integration – for near real-time monitoring of bird interactio
 ns with solar energy infrastructure using AI-enable edge-computing cameras
  deployable at operational large-scale solar energy facilities.\n\nPositio
 n Requirements:\n\nPhD in Computer Science, Environmental Science, Biology
 , or similar.\nExperience in machine/deep learning, computer vision, scien
 tific computing or mathematical optimization.\nProgramming experience in C
 , C++, and/or Python.\nGood skills in sustainable software engineering pra
 ctices such as version control, self-documenting code, unit testing and co
 ntinuous integration.\nContributions to community software packages aligne
 d with the position will be looked on favorably.\nGood communication skill
 s, both verbal and written.\n\nDesirable Knowledge and Skills:\n\nExperien
 ce in applying machine/deep learning approach for ecological/environmental
  research.\nExperience in Keras, Tensorflow, PyTorch, Chainer, or similar 
 packages.\nAbility to understand and implement methods from latest machine
  learning articles.\nExperience and skills in interdisciplinary research i
 nvolving computer scientists and discipline scientists.\nExperience with O
 penCV.\nExperience with parallel programming such as MPI.\nExperience in h
 igh-performance computing.\nCollaborative skills including the ability to 
 work well with other laboratories and universities, supercomputer centers,
  and industry.\nBeing open-minded and ability to acquire new skill set and
  explore new techniques to advance sciences.\n\nRegistration Category: Tec
 h Program Reg Pass, Workshop Reg Pass, Tutorial Reg Pass, Exhibits Reg Pas
 s
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