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UID:submissions.supercomputing.org_SC20_sess280_job138@linklings.com
SUMMARY:Postdoctoral Appointee - Data Science and Learning for End-to-End 
 Biological Programming
DESCRIPTION:Job Posting\n\nPostdoctoral Appointee - Data Science and Learn
 ing for End-to-End Biological Programming\n\n\n\nPosition Description:\n\n
 The Data Science and Learning (DSL), Biosciences (BIO), and X-ray Science 
 (XSD) divisions at Argonne National Laboratory invites applications for th
 ree postdoctoral researchers for developing scalable statistical inference
  approaches for programming biological systems. Biological programming app
 lications (e.g., protein design, synthetic biology) are now increasingly a
 utomated through robotic instrumentation and cloud-lab platforms (e.g., Em
 erald Lab). Our group is particularly interested in exploiting these high 
 throughput capabilities in expanding how biological programming is impleme
 nted in an end-to-end fashion – i.e., multiple iterations of design, build
 , test and learn all run autonomously through AI-enabled infrastructure. \
 n\nWhile many biological programs seek to abstract the design-build-test-l
 earn (DBTL) cycle, they need scalable artificial intelligence (AI) and mac
 hine learning (ML) methods for: (i) representational learning and symbolic
  reasoning to infer biological abstractions from existing data; (ii) activ
 e learning for incorporating current (or partial) observables into biologi
 cal abstractions to yield better design of experiments; and (iii) probabil
 istic model checking to enable robust execution of biological programs. We
  propose to capture these activities as platform for designing robust end-
 to-end biological programs for synthetic biology applications. \n\nWe are 
 interested in bringing together an interdisciplinary team that has experti
 se in AI, high performance computing, robotics and systems/structural biol
 ogy to explore the combinatorics of design involved in studying self-assem
 bly processes with intrinsically disordered proteins and targeting secure 
 biosystems design. \n\nThe candidates will build scalable AI/ML models on 
 data center AI systems (e.g., Cerebras CS-1 ML accelerator and Argonne's A
 urora exascale supercomputer) that are tightly integrated with mechanistic
  modeling frameworks (e.g., MD simulation platforms as well as flux-balanc
 e analysis tools), and implement end-to-end experimental design on Argonne
 ’s robotic platforms. \n\nThe opportunity will involve close collaboration
 s with researchers at the University of Chicago and Northwestern Universit
 y along with other national laboratories including Brookhaven, Livermore, 
 and Berkeley.\n\nPosition Requirements:\n\nPhD in a computer science, phys
 ical sciences or engineering or related field.\nComprehensive experience p
 rogramming in one or more programming languages, such as C, C++, and Pytho
 n\nExperience with machine learning methods and deep learning frameworks, 
 including tensorflow, pytorch.\nExperience in heterogenous programming and
  GPU programming in the machine learning context is valuable.\nSoftware de
 velopment practices and techniques for computational and data-intensive sc
 ience problems. \nExceptional communication skills, ability to communicate
  effectively with internal and external collaborators and ability to work 
 in team environment.\nAbility to model Argonne’s Core Values: Impact, Safe
 ty, Respect, Integrity, and Teamwork. \n\nPreferred:\n\nExperience and und
 erstanding of how synthetic biological circuits function (basic understand
 ing of CRISPR/Cas9 mechanisms)\nExperience in working with structural biol
 ogy applications (especially with disordered proteins).\nExperience in app
 lied machine learning (e.g., successful projects that used ML to solve sci
 entific problems).\nExperience with high-performance computing and handlin
 g robotic platforms (e.g., Hudson Robotics).\nAbility to provide project l
 eadership.\n\nRegistration Category: Tech Program Reg Pass, Workshop Reg P
 ass, Tutorial Reg Pass, Exhibits Reg Pass
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