Postdoctoral Fellow: ML for autonomous materials discovery

Berkeley, CA

Applications have closed
Lawrence Berkeley National Lab logo

Lawrence Berkeley National Lab

Postdoctoral Fellow: ML for autonomous materials discovery - 96563

Organization: AM-Applied Mathematics and Computational Research

 

Lawrence Berkeley National Lab’s (LBNL, https://www.lbl.gov/) Applied Mathematics and Computational Research Division (https://crd.lbl.gov/divisions/amcr/) has an opening for a Postdoctoral Scholar to develop and apply machine learning, optimization, and sampling tools to autonomous materials discovery. You will be part of the Applied Computing for Scientific Discovery Group, which has a focus on enabling scientific discovery through the development of advanced software applications, tools, and libraries in key DOE mission areas, as well as the development of scientific computing applications and capabilities for the integration and analysis of complex data from simulation and experiment. In this position, you will play an integral role in a multidisciplinary team that includes scientists from energy technologies, physical sciences, and computational sciences. You will actively contribute to ACSD’s efforts in machine learning, optimization, and sampling to enable accelerated materials discovery and manufacturing scale-up.

 

This position is a research position combining elements of applied mathematics, optimization, statistics, machine learning, and computational science. The successful applicant will develop, test, and benchmark new optimization models, active learning and sampling strategies, and machine learning models that will close the feedback loop for automated materials discovery. A strong background in high performance computing and programming is a must.

 

What You Will Do:

• Perform basic research in numerical optimization, active learning, and machine learning.

• Develop, test, benchmark, and tune algorithms.

• Deploy developments on high performance computing.

• Work in a multidisciplinary team environment including scientists from energy technologies, physical sciences, mathematics, and computing.

• Author peer-reviewed journal articles and contribute to research proposals.

• Publish developed algorithms as software packages.

 

What is Required:

• Ph.D. in Applied Mathematics, Statistics, Machine Learning, Computational Science or related areas within the last 3 years, with a strong research background in machine learning, numerical optimization, and programming.

• Demonstrated research experience in the development and/or application of machine learning and computational optimization algorithms.

• Excellent Python programming skills.

• Demonstrated experience working on high performance computing.

• Ability and desire to work as part of an energetic cross-disciplinary team.

• Excellent oral and written communication skills.

• Strong interpersonal communication skills.

 

Want to learn more about Berkeley Lab's Culture, Benefits and answers to FAQs? Please visit: https://recruiting.lbl.gov/

 

Notes:

• This is a full-time, 2 years, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.

• This position is represented by a union for collective bargaining purposes.

• Salary will be predetermined based on postdoctoral step rates.

• This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

• Work may be performed on-site or hybrid modes. Work must be performed within the United States.

 

How To Apply

Apply directly online at http://50.73.55.13/counter.php?id=236438 and follow the on-line instructions to complete the application process.

 

Based on University of California Policy - SARS-CoV-2 (COVID-19) Vaccination Program and U.S Federal Government requirements, Berkeley Lab requires that all members of our community obtain the COVID-19 vaccine as soon as they are eligible. As a condition of employment at Berkeley Lab, all Covered Individuals must Participate in the COVID-19 Vaccination Program by providing proof of Full Vaccination or submitting a request for Exception or Deferral. Visit covid.lbl.gov (https://covid.lbl.gov/) for more information.

 

Berkeley Lab is committed to Inclusion, Diversity, Equity and Accountability (IDEA, https://diversity.lbl.gov/ideaberkeleylab/) and strives to continue building community with these shared values and commitments. Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab's mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

 

Equal Opportunity and IDEA Information Links:

Know your rights, click here (https://www.dol.gov/agencies/ofccp/posters) for the supplement: Equal Employment Opportunity is the Law and the Pay Transparency Nondiscrimination Provision (https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf) under 41 CFR 60-1.4.

Tags: HPC Machine Learning Mathematics Python Research Statistics

Perks/benefits: Career development Equity

Region: North America
Country: United States
Job stats:  216  9  0

Other jobs like this

Explore more AI/ML/Data Science career opportunities

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.