Electrical Engineer – L3Harris

Job ID: SAS20200608-48887 

Job Title: Electrical Engineer (PhD Grad – Palm Bay, FL) 

Job Location: Palm Bay, FL 

Job Description: 

• Drive solutions to overcome materials related technical barriers 

• Selection of materials for tool construction (Structural materials, adhesives, lubricants, coatings) • Selection of materials for wafer level devices including metals, dielectrics, tie layers, diffusion  barriers, and superconductors 

• Analysis of root causes for materials failure 

• Analysis of corrosion mechanisms and design of anticorrosion solutions 

• Design and performance analysis of microelectronic devices 

• Oversight and analysis of analytical data, including FTIR, DSC, DMA, TGA, SEM/EDS, TOF SIMS 


• Masters or PhD degree in Electrical Engineering, Computer Engineering, Electrical &  Computer Engineering, Chemical Engineering, Materials Engineering, Materials Science  Engineering, Physics or related field 

• GPA of 3.0 or greater 

Preferred Skills: 

• Familiarity with laser processing of materials 

• Prior experience or education in low temperature physics and superconductivity • Knowledge of materials synthesis methods and prior experience with optical material  characterization methods (e.g., surface measurements, bulk index measurements, nonlinearity  measurements, thermal property measurements). 

Please be aware many of our positions require the ability to obtain a security clearance. Security clearances may  only be granted to U.S. citizens. In addition, applicants who accept a conditional offer of employment may be subject  to government security investigation(s) and must meet eligibility requirements for access to classified information. 


Trailblazers in Engineering – Purdue University


Industrial Software Engineer Position – GE

Job Description Summary

Would you like to help improve the lives of billions of people? Are you excited about contributing to the Future of Flight, Precision Health and Decarbonization? As a Software Engineer at GE Research, you’ll work on multi-disciplinary teams to deliver software that brings value to the real world. You will help identify customer needs, propose technical differentiators and develop modular, scalable software solutions that bring value to society. Connect your passion to purpose and help move, cure and power the future!

Job Description

This is a hands-on software engineering position. You will work on fast-paced, multi-disciplinary teams to solve technical challenges. You will collaborate with scientists/engineers, GE business units, government agencies and end-customers to devise software that meets program requirements. You will be provided ample opportunity to learn on the job, shape your career and define new research areas.

Essential Responsibilities:

  • Problem-solving – be a vital team member on multi-disciplinary programs that take real-world problems and deliver solutions. Program scope can range from quick lab prototypes that validate concepts and reduce risk, all the way up to multi-year programs that deliver robust, efficient and scaled-up software systems directly to customers
  • Coding – contribute to technology-differentiated software that may include simulations, scripting, statistics, machine learning, computer vision, text-mining, visualization, web interfaces, search, databases, etc.  with platforms spanning embedded, desktop, controllers and cloud
  • Quality — optimize quality/time/cost by making smart choices and technology tradeoffs, driven by requirements. Adopt best-practices in code documentation, refactoring, testing, modularity, reusability and scalability, based on program maturity and customer need


  • PhD in Computer Science, Computer Engineering or a related discipline OR Master’s Degree in Computer Science, Computer Engineering or a related discipline with a minimum of 2 years of experience in software design and development OR Bachelor’s Degree in Computer Science, Computer Engineering or a related discipline with a minimum of 6 years of experience in software design and development
  • Strong problem-solving skills with demonstrated ability to take high-level customer needs, covert to software strategy and requirements, and execute to completion
  • Strong knowledge of object-oriented design, software design patterns and principles
  • Strong expertise in two or more programming languages e.g. Python, C++/.Net, Bash, R, Perl, Java, C++ , Play, Angular JS, OSGI, HTML5, C, C#
  • Strong expertise in Unix/Linux, Git, Javascript and Docker. Windows a plus.
  • Legal authorization to work in the U.S. is required. We will not sponsor individuals at the Masters or Bachelors level for employment visas, now or in the future, for this job opening.
  • Because of the specific categories of data handled by GE Research and the structure of our work environment, we are unable to accommodate employment of persons while they are considered nationals of embargoed countries subject to restriction under the US Export Administration Regulations (EAR), 15 CFR Section 746 et seq. (currently North Korea, Syria, Iran, Cuba, and Sudan). Please note that citizens of embargoed countries who have either U.S. person” status under U.S. export control laws or subsequent citizenship from a non-embargoed country can be considered.
  • Must be willing to work out of an office located in Niskayuna, NY
  • Must be 18 years or older
  • You must submit your application for employment on the careers page at to be considered.

Desired Characteristics:

  • Domain Expertise – strong grasp of physical systems e.g. Advanced Manufacturing, Robotics, Internet-of-Things, Biomedical Devices, Aerospace, Automated Inspections, etc.
  • System-thinking – experience architecting/building software systems with multiple components e.g. UI, algorithms, multi-modal data and visualization. Bonus: edge-devices to controller to cloud
  • Resourcefulness – broad range of tools & skills that can be tapped to deliver effective solutions e.g. R, TensorFlow/Keras/PyTorch, NumPy, Matlab, Julia, cloud computing, databases, parallel programming, etc.
  • Agile/Lean– experience developing and executing test plans in a CI/CD environment, including source code control, automated unit and regression testing
  • Self-starter – passionate about identifying, defining and pursuing new ideas to make an impact
  • Team Player — good collaborator and clear communicator. Plays an active role in the continuous improvement of the team through outreach, continuous-learning and peer teaching
  • Recognition – widely recognized as an expert in the field e.g. a) contribution to public domain codebases and/or significant github portfolio b) peer-reviewed publications and invited presentations, c) winning and executing on peer-reviewed government contracts
  • Leadership Behaviors – Act with Humility, Lead with Transparency, and Deliver with Focus … and always with unyielding integrity

GE offers a great work environment, professional development, challenging careers, and competitive compensation. GE is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.

GE will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).

Relocation Assistance Provided: Yes


Dissertation Defense – Hanlong Wan


Author: Hanlong Wan

Date/Time: 9/2/21 | 2:00PM-4:00PM

Location/Room: EGR4164B Martin Hall (CEEE’s conference room).

Advisory Committee:
Prof. Reinhard K. Radermacher, Chair
Prof. Peter Sunderland, Dean’s Representative
Research Prof. Yunho Hwang
Prof. Nikhil Chopra
Prof. Jelena Srebric
Prof. Bao Yang

Energy consumption of Heat Pump (HP) systems plays a significant role in the world residential building energy sector. The conventional HP system evaluation method focused on the energy efficiency during a given time scale (e.g., hourly, seasonally, or annually). Nevertheless, these evaluation methods or test metrics are unable to properly reflect the thermodynamic characteristics of the system (e.g., the start-up process). In addition, previous researchers typically conducted HP field tests within one year period. Only limited studies conducted the system performance over multiple years. Furthermore, the climate is changing faster than previously predicted beyond the irreversible and catastrophic tipping point. HP systems are the main contributor to global warming but also can be a part of the solution. A holistic evaluation of the HP system’s global warming impact during its life cycle needs to account for the direct refrigerant greenhouse gas (GHG) emissions, indirect fossil fuel GHG emissions and embodied equipment emissions. This dissertation leverages machine learning, deep learning, data digging, and Life Cycle Analysis (LCA) approaches to develop next generation HP system evaluation methodologies with three thrusts: 1) field test data analysis, 2) data-driven modeling, and 3) Enhanced Life Cycle Climate Performance (En-LCCP) analysis. This study found that first, time-average performance metrics can save time in extensive data calculation, while quasi-steady-state performance metrics can elucidate some details of the studied system. Second, deep-learning-based algorithms have higher accuracy than conventional modeling approaches and can be used to analyze the system’s dynamic performance. However, the complicated structure of the networks, numerous parameters needing to be optimized, and longer training time are the main drawbacks of these methods. Third, this dissertation improved current environmental impact evaluation method considering ambient conditions variation, local grid source structure, and next-generation low-GWP refrigerants, which led the results closer to reality and provided alternative methods for limited-data cases. Future work could be studying the uncertainty within the deep learning networks and a general process for modeling settings. People may develop a multi-objective optimization model for HP system design considering both the LCCP and cost.


Dissertation Defense – Mahsa Doosthosseini

Title: Analysis and Optimization of Input Trajectories for Parameter Identifiability in Multi-Compartment Dynamic System Models

Author: Mahsa Doosthosseini

Date/Time: 08/17/2021  12:30 pm-2:30 pm

Examining Committee:
Dr. Hosam K. Fathy  (Chair/Advisor)
Dr. Alisa Morss Clyne (Dean’s representative)
Dr. Joseph S. Friedberg
Dr. Jin-Oh Hahn
Dr. Simona Onori
Dr. Monifa Vaughn-Cooke

Zoom Link:

This dissertation examines the interconnected problems of (i) analyzing and (ii) optimizing the impact of a multi-compartment dynamic system’s input history on the identifiability of its parameters. Identifiability refers to the feasibility and accuracy with which a system’s parameters can be uniquely estimated from input-output test data. The shape of a system’s input history versus time often affects identifiability. This makes it possible to optimize this input shape for identifiability, in a manner analogous to the use of a cardiac stress test to better diagnose patients with heart disease.

The research in this dissertation makes four contributions to the literature, motivated by the following four practical research questions. First, is it possible to characterize CO2 gas transport dynamics in a laboratory animal where the peritoneal perfusion of a perfluorocarbon (PFC) is used as a potential treatment for hypercarbia? Second, how does the shaping of chemotherapeutic treatment affect the accuracy with which drug resistance dynamics can be estimated in a partially drug-resistant cancerous tumor? Third, can the dynamic cycling of a lithium-sulfur (Li-S) battery be tailored to maximize the accuracy with which its parameters are estimated? Finally, can Pontryagin methods from optimal control theory yield fundamental insights into the structure of the ambient temperature cycling trajectory that maximizes the identifiability of a lithium-ion battery model’s thermal parameters?

In addressing the above practical research questions, this dissertation navigates a progression of four fundamental topics in the field of multi-compartment dynamic system parameter identification and identifiability. Specifically, the dissertation’s examination of peritoneal CO2 gas transport dynamics highlights and motivates the importance of analyzing multi-compartment dynamic system identifiability. The subsequent examination of the identifiability of drug resistance dynamics in cancerous tumors highlights the degree to which input shaping can negatively affect parameter identifiability. In contrast, the examination of parameter identifiability for Li-S batteries highlights the potential of input shaping to improve identifiability significantly for multi-compartment systems. Finally, the dissertation’s examination of thermal battery parameter identifiability highlights the degree to which the fundamental tool of Pontryagin analysis can help gain insight into optimal input shaping for identifiability. In summary, the work in this dissertation explores a progression of fundamental topics in the area of dynamic system parameter identifiability while highlighting the broad applicability of this area to different practical domains.


Dissertation Defense – Weiping Diao


Author: Weiping Diao 

Day/Time: 08/26/2021 10:00 AM – 12:00 PM EST

Examining Committee: 
Dr. Michael Pecht, Chair
Dr. Chunsheng Wang, Dean’s Representative
Dr. Michael Azarian
Dr. Hosam Fathy
Dr. Paul Albertus
Dr. Stanislav I. Stoliarov


Commercialization of lithium-ion batteries has enabled applications ranging from portable consumer devices to high-power electric vehicles to become commonplace. The capacity, which has been used to determine if lithium-ion batteries have reached the end of life, decreases over usage (cycling) and storage (rest). An increase in the capacity fade rate after some charge-discharge cycles is often observed in lithium-ion batteries. The phenomenon has been described as a knee point and can lead to a shorter life than expected. 

Although the general degradation modes, mechanisms, and effects on lithium-ion batteries are known, the dominant degradation modes and mechanisms for the knee point phenomenon have yet to be determined. Understanding why and when the knee point will appear on the capacity fade curves is valuable to battery manufacturers and device companies to predict or mitigate the knee point. This study presents the degradation behavior with knee point identification algorithms, accelerated testing and capacity modeling methods to assess the degradation and predict the knee point, and experimental analysis which identify the dominant degradation modes and mechanisms. 


Intel Job Opening for PhD Candidates

The Portland Technology Development group of Intel Corporation currently has openings for physical science Ph.D.s to support/direct R&D of advanced processing methods. Candidates hired for these positions will be responsible for developing the next generation of Intel’s microprocessors.

Ph.D. candidates in Physics, Chemistry, Materials Science, Chemical Engineering, Electrical Engineering, Mechanical Engineering, or related fields are encouraged to apply. Criteria for selection include: a strong academic record, demonstrated experimental and data analysis expertise, superior critical thinking skills, an ability to drive and take responsibility for projects and a solid peer-reviewed publication record. Experience using and maintaining scientific equipment is preferred. Semiconductor processing experience is not mandatory.

Openings are immediately available at Intel’s primary development facility (Ronler Acres) located ~10 miles west of Portland, OR. Please see a more detailed job description included below. 

Interested candidates should email resumes to

Ricardo Nava

PTD Intel Corporation

Job Description:

PTD Module Engineers are responsible for leading scientific research enabling manufacture of innovative device architectures coupled with the realization of these architectures. Designing, executing and analyzing experiments necessary to meet engineering specifications for their process. Participate in development of intellectual property. Development of the equipment necessary to exploit the understanding gained in research (in collaboration with equipment suppliers). Work effectively with the equipment supplier to identify shortcomings, propose and evaluate hardware modification to mitigate issues. Operate manufacturing line in order to integrate the many individual steps necessary for the manufacture of complex microprocessors. Insitu ramp to manufacturing volumes to demonstrate the technology meets requirements while simultaneously transferring the technology to counterparts in manufacturing via the Copy Exactly! Methodology. Install and qualify manufacturing capacity at the development site and audit installation/qualification and supervise first full loop at the production site.

This is an entry-level position and compensation will be given accordingly


You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Experience listed below would be obtained through a combination of your school work/classes/research and/or relevant previous job and/or internship experiences.

Minimum Qualifications:

  • Candidate must possess a Master’s degree (with a minimum of 2 years of experience) or a PhD degree(with a minimum of 1 year of experience) in Electrical Engineering, Chemical Engineering, Material Science, Physics or Chemistry or a related field with experience in one or more of the following:
  • Semiconductor materials and characterization
  • Statistical Process Control (SPC) or Design of Experiments (DOE) principles and engineering analysis tools
  • Analytical Techniques

Job Type:

College Grad


Shift 1 (United States of America)

Primary Location:

US, Oregon, Hillsboro

Business group:

Intel strives to make every facet of semiconductor manufacturing state-of-the-art — from semiconductor process development and manufacturing, through yield improvement to packaging, final test and optimization, and world class Supply Chain and facilities support.  Employees in the Technology and Manufacturing Group are part of a worldwide network of design, development, manufacturing, and assembly/test facilities, all focused on utilizing the power of Moore’s Law to bring smart, connected devices to every person on Earth

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.


Energy Consultant Position – OnLocation

Energy Consultant Position
: Washington, DC

Job Code: 1264

DescriptionKeyLogic has an immediate need for an Energy Consultant to support our newly-acquired company, OnLocation. OnLocation is recognized as a leading energy consulting firm providing objective quantitative analysis to a diverse set of stakeholders in establishing energy, environmental, and climate change policies for the U.S. since 1984. Our mission is to use quantitative analytical methods to objectively inform strategic players in energy and related industries to improve the outcome of business and policy decisions as the USA strives for a balanced and sustainable energy future. To help our clients understand the implications of the challenges facing our energy system, we develop, modify and apply a variety of computer models and data analysis tools to examine energy trends, impacts of proposed government policies, and the associated financial and economic impacts of energy investment decisions.

 Position Requirements:

  • Bachelor’s Degree in Economics, Engineering, Finance, Mathematics, Statistics, or Energy Policy; advanced degree is a plus
  • 0-5 years of relevant energy-related experience in industry, consulting, government, or non-governmental organization
  • Excellent technical, analytical, and quantitative skills
  • Strong Microsoft Excel, Word, and PowerPoint skills
  • Strong experience with R/Python or other tools is a plus
  • Creative problem-solving abilities
  • Interest in and knowledge of energy issues and policy analysis
  • Excellent verbal and written communication skills
  • Ability to work in a team setting as well as independently

Additional Desired Qualifications:

  • Ability to communicate complex ideas and summarize large data sets through effective tables, charts, and summary discussions
  • Good organizational skills, including documentation of assumptions and processes
  • Good attention to detail and QA/QC habits
  • Hands-on energy modeling experience and/or data analytics
  • Knowledge of operations research and/or other programming skills

Position Responsibilities:

  • Assist in designing and implementing quantitative research and consulting studies using various energy models
  • Assist in developing and improving analytical tools and techniques to address client needs
  • Work with large data sets, including historical market information, detailed forecasting model results, and modeling inputs
  • Create written reports and PowerPoint presentations for clients
  • Develop industry expertise with the goal of becoming an industry expert in one or more areas
  • Participate in conference calls and meetings with clients

Any questions should be directed to the contact below: 
Jeannine Ogden | Talent Acquisition Specialist443.539.9047 (direct) | 304.296.9100 (main) | 304.296.9300 (fax) |


Dissertation Defense – Paul Nation


Author: Paul Nation

Day/Time: Monday, August 23rd from 1:00 pm to 3:00 pm

Examining Committee:
Professor Mohammed Modarres, Chair and Advisor
Professor Aris Christou
Assistant Professor Katrina Groth
Professor Jeffrey Hermann
Doctor Martin Wayne, Special Advisor
Professor Gregory Baecher, Dean’s Representative


This research aims to present a Bayesian model for reliability growth planning of discrete-use systems suitable for use throughout all stages of system development. Traditional discrete-use models for reliability growth utilize test data from individual test events at the current stage of development. They often neglect the inclusion of historical information from previous tests, testing similar systems or elicitation of expert opinion. Examining and using data attained from prior bench analyses, sub-system tests or user trial events often fails to occur or is conducted poorly. Additionally, no current approach permits the probabilistic treatment of the initial system reliability at the commencement of the test program in conjunction with the management variables that may change throughout the execution of the test plan.

This research contributes to the literature in several ways. Firstly, a new Bayesian model is developed from first principles which considers the uncertainty surrounding discrete-use systems under delayed and arbitrary corrective action regimes to address failure modes. This differs from current models that fail to address the randomized times that corrective actions to observed failure modes may be implemented depending on the selected management strategy. Some current models only utilize the first observed failure on a test, meaning a significant loss of information transpires as subsequent failures are ignored. Additionally, the proposed strategy permits a probabilistic assessment of the test program, accounting for uncertainty in several management variables.

The second contribution seeks to extend the Bayesian discrete-use system model by considering aspects of developmental, acceptance and operational testing to allow the formulation of a holistic reliability growth plan framework that extends over the entire system lifecycle. The proposed approach considers the posterior distribution from each phase of reliability growth testing as the prior for the following growth test event. The same methodology is then employed using the posterior from the final phase of reliability growth testing as the prior for acceptance testing. It then follows that the acceptance testing posterior distribution forms the prior for subsequent operational testing through a Bayesian learning method. The approach reduces unrealistic and unattainable reliability demonstration testing that may result from a purely statistical analysis. The proposed methodology also permits planning for combined developmental and acceptance test activities within a financially constrained context.

Finally, the research seeks to define an approach to effectively communicate developmental system reliability growth plans and risks to decision-makers. Reliability professionals, like many of their other specialist science peers, are fantastic communicators – with other reliability practitioners. However, when reliability professionals move beyond their world to make an impact, they often face the same challenge scientists from every discipline face – the difficulties of clearly communicating science to their audience. The research presents approaches that utilize the vital communication, influence and emotional intelligence skills that are necessary for motivating decision-makers and colleagues who can assist in supporting and implementing reliability engineering efforts.

Fellowships & Scholarships

SMART Scholarship Program

The SMART Scholarship is a program that allows students (undergraduate and graduate) to partner with various DoD facilities across the U.S. to conduct research with government employees. 

The benefits of the scholarship include tuition and stipend in addition to guaranteed summer internships and a job after graduation! See the flyer attached for more information. 

The application period opened on August 1st, and runs through the end of November!  If you are interested in the program and would like to learn more  feel free to check out the website or email Michael Hitt ( for more information.