2021 Internship Placements

Heila Technologies is an MIT-born startup dedicated to simplifying the integration and operation of individual Distributed Energy Resources (DERs) and microgrids. Low cost, low carbon, local energy is within our reach. The Heila Platform opens the energy ecosystem to innovation and community action, making it possible to grow renewable energy-based microgrids from the ground up.

(1 internship, 1 intern will be placed) 

Heila Technologies is looking to hire a Data Scientist Intern to create and develop reports that provide insight and analysis to the customers of our control and optimization solution, thus supporting a higher adoption of low-carbon, distributed energy resources (DERs).

Responsibilities: 

An ideal candidate will have a strong desire to learn and build reports in a collaborative team environment. The types of tasks you will have the opportunity to work on include:

• Collaborate with the Optimization and Control and Sales teams to understand their goals and objectives
• Use programming skills to explore, analyze and interpret large volumes structured and unstructured data
• Utilize predictive modeling and statistical analysis to determine analytical approaches and evaluate scenarios as well as potential future outcomes
• Generate insights connecting analytical results with business problems or objectives
• Present and provide recommendations to internal teams and stakeholders

Requirements:

• Experience with data science techniques including mathematical and statistical analyses, modeling and data visualization
• Knowledge of R or Python and SQL
• Strong collaboration and communication skills within and across teams

Preferred/relevant majors include Statistics/Biostatistics, Mathematics, Computer Science, Data Science/Health Data Science, Engineering

The length of this internship is 12 weeks.

Founded in 2013, InTech Energy provides software solutions for utilities, energy service providers, energy evaluators and building managers to save energy and reduce costs and carbon emissions in commercial buildings as well as supporting cost effectiveness of Energy Efficiency and Distributed Energy Resource programs.

(1 internship, 2 interns will be placed) 

InTech is seeking to enhance the analytic capabilities of our Energy360® energy management technology (EMT)to better identify and quantify changes in building equipment and energy use operations due to non-routineevents, anomalies, or planned energy efficiency projects. The goal is to implement a minimally intrusive data collection process that uses building meta-data (size, usage, HVAC system type, etc.), weather data, and theprimary utility meter data, and minimal additional energy monitoring or sensoring, that can be used on individual buildings, i.e., we are not seeking a population-based analysis.

The anticipated role is a combination of data science and an understanding of the physical nature of building operations. The data scientist would access InTech’s and other historical building energy usage databases (US Dept. of Energy, US Energy Information Administration, Energy Star, Utility studies, Electric Power Research Institute studies, etc.) to discover patterns for normal and abnormal energy consumption that can be repeatedly used in analyzing, measuring and forecasting energy use in buildings.

Requirements:

Strong background in creative data analyses, and working with large data sets using a variety of techniques to solve a real-world problem. Must have practical experience in scaling creative solutions for production scale in a software product environment. Experience can be shown with class projects or independent projects. Education in Data Science and related areas would be very helpful.

Other preferred qualifications:

The ideal candidate will also understand the basic physical nature of building operations, e.g. understanding how factors like weather, building usage, and building occupancy influence energy consumption.

At Johnson Controls, we transform the environments where people live, work, learn and play. From optimizing building performance to improving safety and enhancing comfort, we drive the outcomes that matter most. Dedicated to protecting the environment, we deliver our promise in industries such as healthcare, education, data centers, and manufacturing.

(1 internship, 3 interns will be placed) 

We will be looking to have interns assist on several projects that will help us expand the offering of a SaaS based application that provides visibility and improvement into the performance of buildings and facilities. Buildings consume 40% of the overall energy in the world. Our applications help to improve the performance of facilities while improving the experience of the occupants in these spaces. We are looking to expand the value proposition of our offering and we are looking for assistance across the areas below:

1. Assist with the development of our Healthy Buildings specification. We are working to have a broader definition and scoring of a healthy building that will support our 3 pillars of healthy places, people and planet. We need assistance in to improve our calculation methodologies and scenarios that go into our scoring model.
2. We are expanding our value proposition to improve the space utilization within the building. We have a feature set that understands occupancy, reservations and hot desking data from and occupant experience application that is used by occupants in the building. We are looking to create a suite of analytics using Power BI to understand the overall utilization and correlate that information to with Comfort and Energy.
3. We are looking to have our application provide information that will help our customers with Sustainability. We need assistance in understanding this space and helping to develop the specification document that will be used to build out the features in the application. Part of the specification will be to identify specific outcomes/actions that can be shown in our applications.
4. As part of our suite of offerings we have an optimization solution that improves the performance of Central Utility Plants. We are looking for someone to help to baseline calculations and show the improvements seen from the use of our application. This will lead into building out case studies and market collateral material to support the offering.

Requirements:

• Engineering or related discipline with focus on energy or sustainability
• Market sizing analysis is a plus
• Self Motivated
• Ability to work in a team
• Strong writing and communication skills

Internship duration: 10-12 weeks, starting May or June (flexible)

Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 202-acre site in the hills above the UC Berkeley campus that offers spectacular views of the San Francisco Bay, Berkeley Lab employs approximately 3,232 scientists, engineers and support staff. 

(5 internship positions, 5 interns will be placed)

Transfer Learning for Advanced Building Controls

Location: Berkeley, CA

Supervisors: Tianzhen Hong, Building Technology and Urban Systems Division

Machine learning (ML) models have demonstrated excellent performance in optimizing building controls for energy efficiency and demand flexibility. However, transfer learning is a challenge in ML based building controls as a very limited number of buildings have adequate and good-quality data for training and validating ML models. Transferring these ML models to other buildings with limited data will require effective algorithms which are structured in a way that bridges two different buildings capturing their common and different characteristics (e.g., energy system type, operation strategies, climates). The intern will help review transfer learning algorithms and choose a few to test for reinforcement learning based building controls.  One intern is needed.

The intern is required to have knowledge of machine learning especially reinforcement learning and understand transfer learning. It is a plus if the intern knows how to use popular ML toolkits for Python coding and testing ML models. The intern will be part of a project team presenting and discussing progress weekly. Depending on the outcomes, there is a potential for the intern to contribute to the writing of a journal article. 

Work location: LBNL main campus (assuming COVID-19 is over by the summer - if not then some/all work may become remote).

Working period: 8 weeks

Preferred start date: flexible start from mid May to early July of 2020.

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Solid-State Battery Intern

Location: Berkeley, CA

Supervisors: Gao Liu, Applied Energy Materials Group

The Applied Energy Materials group at ESDR is looking for one materials sciences intern. The intern will perform experimental materials research on solid-state battery for electric vehicle applications. The intern will synthesis new solid-state ion conductors, fabricate batteries based on those ion conductors, and testing the battery cells. 

This is an experimental internship.

Work location: LBNL main campus in Building 70 (assuming COVID-19 is over by the summer - if not then some/all work may become remote). 

Preferred start date: beginning of summer. 

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New Insights into Advanced Membranes for Energy Applications

Location: Berkeley, CA

Supervisors: Gregory Su

New materials with tailored functionality are needed for renewable energy technologies. Membranes enable the selective transport of specific species are critical components in these technologies, for example, the proton conducting membrane in a hydrogen fuel cell. Membranes made of polymers are attractive due to their tunable chemistries and favorable mechanical properties. However, we still need to better understand how molecular architecture dictates the nanoscale assembly of polymer molecules and ultimately membrane performance. This internship will focus on probing the nanoscale structure of membranes using unique experimental X-ray tools coupled with simulations and other characterization methods to gain new insights into membrane structure-property relationships. The work will involve working closely with research groups at Berkeley Lab and take advantage of Berkeley Lab's world-class facilities, including the Advanced Light Source and the Molecular Foundry.

Number of interns required: 1

Work location: LBNL main campus (assuming COVID-19 is over by the summer - if not then some/all work may become remote). 
Preferred start date: dates flexible during the summer of 2021

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Bioenergy Quantum Sensing

Location: Berkeley, CA

Supervisors: Benjamin Gilbert

We seek one motivated undergraduate intern to contribute to the development of a new chemical imaging method to optimize biofuel production in bioreactors. The candidate will work with a multidisciplinary team of scientists in Earth & Environmental Sciences and Biosciences Divisions at Lawrence Berkeley National Laboratory (LBNL). Our groups are collaborating on using quantum sensing and complementary methods to study microbial metabolic processes, ultimately for the in-situ observation of single-cell biochemistry. Depending on background, and onsite or virtual intern, the candidate will design optical, microfluidics or microwave instrumentation, develop python software for data acquisition, or acquire and process chemical imaging data from microorganisms using a diamond-based quantum sensor, a confocal fluorescence microscope or a Raman microscope. 

Motivated candidates from the core STEM fields will be considered, and preference will be given to the ones with relevant experiences and skills. 

Work location: LBNL main campus (assuming COVID-19 is over by the summer - if not then some/all work may become remote).

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Sustainable Energy System Intern

Location: Remote

Supervisors: Bin Wang, Energy Analysis and Environmental Impact Division

As our infrastructure systems, novel and scalable decision-making methodologies are needed to resolve the 1) interdependency among different systems, and 2) interactions with the complex climate events, e.g. the wildfire, within the planning and operation problems.    One intern is expected to assist the existing LDRD projects. 

• Build future (year 2030, 2040) electric vehicle travel demand models for the San Francisco bay area  
• Validate optimization models with real-world data and scenarios  
• Collect historical climate (events) data and build predictive models to project climate impact on the existing infrastructure systems  
• Build economic metrics to quantify the economics of the wildfire impacts on the electric grid  

Basic programming and data analytic skills are required. Candidates with experience in economics, machine learning, optimization are preferred. 

Work location: work from home is expected. 

(6 internship positions, 6 interns will be placed)

Project Management and Strategy Intern

Location: Remote

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Communications and Marketing Intern

Location: Remote 

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Data Management Intern

Location: Remote

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Front End Engineering Intern

Location: Remote

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Software Engineering Intern

Location: Remote

Takachar is an MIT spinout company with a test site in Berkeley, CA. The company's vision is to expand the amount of biomass (crop and forest residues) economically converted into useful products (we turn trash into cash). Our impacts include income and job creation in rural areas, as well as mitigation of CO2 emission and air pollution associated with conventional open-air biomass residue burning. If successful, this could also potentially reduce the risk of catastrophic wildfires.

(1 internship, 1 intern will be placed) 

In summer 2021, we will be building and testing an internal prototype. We are seeking one project engineer to coordinate this effort. The engineer will assist in testing the prototype, first in a laboratory setting, and also potentially in the field. In the latter stage of the project, you should be prepared to work in a rural environment (in California) under resource-constrained settings.

Requirements:

Prior experience with SolidWorks, design for manufacturing, control systems, and reactor testing will be helpful. A project website (if you have one) will help us gauge your experience.