The Data Science and Learning (DSL), Biosciences (BIO), and X-ray Science (XSD) divisions at Argonne National Laboratory invites applications for three postdoctoral researchers for developing scalable statistical inference approaches for programming biological systems. Biological programming applications (e.g., protein design, synthetic biology) are now increasingly automated through robotic instrumentation and cloud-lab platforms (e.g., Emerald Lab). Our group is particularly interested in exploiting these high throughput capabilities in expanding how biological programming is implemented in an end-to-end fashion – i.e., multiple iterations of design, build, test and learn all run autonomously through AI-enabled infrastructure.
While many biological programs seek to abstract the design-build-test-learn (DBTL) cycle, they need scalable artificial intelligence (AI) and machine learning (ML) methods for: (i) representational learning and symbolic reasoning to infer biological abstractions from existing data; (ii) active learning for incorporating current (or partial) observables into biological abstractions to yield better design of experiments; and (iii) probabilistic 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.
We are interested in bringing together an interdisciplinary team that has expertise in AI, high performance computing, robotics and systems/structural biology to explore the combinatorics of design involved in studying self-assembly processes with intrinsically disordered proteins and targeting secure biosystems design.
The candidates will build scalable AI/ML models on data center AI systems (e.g., Cerebras CS-1 ML accelerator and Argonne's Aurora exascale supercomputer) that are tightly integrated with mechanistic modeling frameworks (e.g., MD simulation platforms as well as flux-balance analysis tools), and implement end-to-end experimental design on Argonne’s robotic platforms.
The opportunity will involve close collaborations with researchers at the University of Chicago and Northwestern University along with other national laboratories including Brookhaven, Livermore, and Berkeley.
- PhD in a computer science, physical sciences or engineering or related field.
- Comprehensive experience programming in one or more programming languages, such as C, C++, and Python
- Experience with machine learning methods and deep learning frameworks, including tensorflow, pytorch.
- Experience in heterogenous programming and GPU programming in the machine learning context is valuable.
- Software development practices and techniques for computational and data-intensive science problems.
- Exceptional communication skills, ability to communicate effectively with internal and external collaborators and ability to work in team environment.
- Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
- Experience and understanding of how synthetic biological circuits function (basic understanding of CRISPR/Cas9 mechanisms)
- Experience in working with structural biology applications (especially with disordered proteins).
- Experience in applied machine learning (e.g., successful projects that used ML to solve scientific problems).
- Experience with high-performance computing and handling robotic platforms (e.g., Hudson Robotics).
- Ability to provide project leadership.
As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
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