Computational Research Scientist

📁
Research/Science
💼
BU-Bldg Technology Urban Systems
📅
86836 Requisition #

Lawrence Berkeley National Laboratory (LBNL) is seeking a highly skilled Research Scientist to conduct R&D for the Building Technology Department in the Building Technology and Urban Systems Division (BTUS). The department has roughly 80 research staff, including scientists, technicians, students and visitors, all working on the development and demonstration of new technologies for energy-efficient residential and commercial buildings. The historic and core technology areas of interest include, but are not limited to, building performance modeling and simulation, windows & day lighting, envelope materials, sensors and controls, HVAC, and lighting — and the integration of these technologies and systems. A major focus is in developing and deploying software and simulation tools to understand the performance of these technologies both individually as well as integrated into whole-building systems. A new direction for this department is to expand from single buildings to groups of buildings, looking at the performance of campuses, neighborhood districts, and larger-scale systems at the urban level.


The successful candidate will conduct research, and assist in developing and deploying new modeling capabilities to simulate occupant behavior in buildings and their impact on building performance to support various project needs. The incumbent will employ skills of machine learning to analyze datasets of building performance and make better predictions. The successful candidate will also publish research results in peer-reviewed journals and present findings at meetings and conferences. The Research Scientist will also conduct research on topics related to the simulation of energy performance of buildings, and contribute to writing proposals to secure funding in these and related areas.


What You Will Do:

  • Use GAN (Generative Adversarial Network) techniques to cluster electric load shapes for residential and commercial buildings.

  • Use deep learning algorithms to estimate and predict occupancy, plug-loads and internal heat gains in commercial buildings that feed into model predictive controls.

  • Use deep reinforcement learning algorithms to optimize control strategies for minimizing energy cost while enhancing occupant wellbeing and building-grid integration.

  • Contribute to the development of research proposals on occupant modeling and using machine learning to improve building performance.

  • Publish research results in journals, present findings at national and international conferences and support industry on adopting the research outcomes and tools.

  • Develop methods to predict occupant count using existing building infrastructure, e.g., Wi-Fi.

  • Develop methods to model and simulate occupant behavior at the community or city scale.

  • Develop models and tools to predict occupant-driven loads capturing their diversity and stochastics.

What is Required:

  • Advanced architecture or engineering degree or related area relevant to building science, technology, and occupant comfort. Minimum two years working experience.

  • In-depth knowledge and experience with architectural and/or mechanical systems in buildings.

  • Hands-on experience using machine learning techniques for data analytics.

  • Broad knowledge about energy efficiency in buildings.

  • In-depth knowledge about occupant comfort and behavior.

  • Generate new ideas for research proposal development.

  • Demonstrated ability to write high-quality journal publications.

  • Excellent verbal and written communication as well as presentation skills.

  • Capable of working on multiple tasks and projects.

Additional Desired Qualifications:

  • PhD with up to five years research experience in architectural engineering, or building science and technologies.

  • Experience in using artificial intelligence and machine learning techniques and tools to solve building energy problems.

  • Demonstrated expertise in interdisciplinary nature of human comfort and behavior through project activities or journal/conference publications.

  • Strong knowledge of building science, machine learning, and human behavior.

  • Strong knowledge of programming languages, e.g., Python, Modelica.


  • Self-motivation to learn new knowledge to meet emerging research needs.  


The posting shall remain open until the position is filled, however for full consideration, please apply by close of business on May 23, 2019.


Notes:

  • This is a full time 2-year, career-track term appointment that may be renewed to a maximum of five years and that may be converted to career based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs.

  • Full-time, M-F, exempt (monthly paid) from overtime pay.

  • 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.


Berkeley Lab (LBNL) addresses the world’s most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Lab’s scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energy’s Office of Science.


Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4.  Click here to view the poster and supplement: "Equal Employment Opportunity is the Law."


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Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4. Click here to view the poster and supplement: "Equal Employment Opportunity is the Law."

 

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The Lawrence Berkeley National Laboratory provides accommodation to otherwise qualified internal and external applicants who are disabled or become disabled and need assistance with the application process. Internal and external applicants that need such assistance may contact the Lawrence Berkeley National Laboratory to request accommodation by telephone at 510-486-7635, by email to eeoaa@lbl.gov or by U.S. mail at EEO/AA Office, One Cyclotron Road, MS90R-2121, Berkeley, CA 94720. These methods of contact have been put in place ONLY to be used by those internal and external applicants requesting accommodation.