Postdoc for Visualization for Scientific Machine Learning
Berkeley Lab’s Machine Learning and Analytics Group in the Scientific Data Division has a new opening for a Postdoctoral Fellow in Visualization for Scientific Machine Learning. In this exciting role, you will conduct fundamental and applied research in visualization and scientific machine learning, as well as related areas including visual analytics, dimension reduction, feature detection (e.g., via topological data analysis), and/or high-performance computing.
What You Will Do:
- Perform research on visualization for scientific machine learning.
- Design new visualization methods for understanding machine learning models.
- Disseminate research results by publishing and presenting in high-impact journals and top conferences in the areas of visualization, machine learning and artificial intelligence.
Additional Responsibilities as needed:
- Develop visual analytics approaches incorporating newly developed visualization and dimension reduction methods.
- Apply dimension reduction and feature detection, e.g., via topological data analysis, to high-dimensional diagnostics of machine learning models, such as loss functions.
- Develop high-performance algorithms for visualization and data analysis of machine learning models.
What is Required:
- PhD degree, within the last 3 years, in Computer Science, Applied Mathematics, or a related technical field.
- Experience in visualization and machine learning.
- Proficiency in Python and/or related language.
- Proficiency in C++.
- Ability to publish in top journals and conferences.
- Ability to conduct research in a highly collaborative environment.
- Excellent verbal and written communication skills.
- Experience in feature-based visualization, e.g., via topological data analysis.
- Experience with dimension reduction methods.
- Experience in developing visual analytics systems.
- Experience with one or more of the machine learning libraries: PyTorch, Tensorflow, Jax.
- Experience with data analysis libraries, for example, scikit-learn.
- Ability to design and implement visualization and feature-dection algorithms on shared- and distributed-memory parallel platforms.
- Software engineering tools: make, CMake, revision control systems (such as git).
Want to learn more about Berkeley Lab's Culture, Benefits and answers to FAQs? Please visit: https://recruiting.lbl.gov/
- This is a full-time, 2 year, 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 will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.
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 that vaccination requirements have been met or submitting a request for Exception or Deferral. Visit covid.lbl.gov for more information.
Berkeley Lab is committed to Inclusion, Diversity, Equity and Accountability (IDEA) 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.
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