PhD Students for Advanced Process Control and Optimization Research Engineer

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Join a multidisciplinary team of applications engineers who leverage emerging technologies & tools to push the boundaries of existing capabilities and develop the next generation of automation solutions. Process Control and Optimization Engineers contribute to the design, implementation, and sustainment of the advanced automation applications that enable our production and manufacturing facilities to run safely, profitably, and, one day, autonomously. These engineers also participate in strategic research projects to advance company competitive advantage in the autonomous operations space. These applied research and project execution-focused assignments will expose the candidates to the real-life challenges associated with the design and implementation of innovative advanced control, automation, modeling, and real-time optimization solutions for large, complex processes. The job role is in the ExxonMobil Technology and Engineering organization, which provides technical expertise to existing facilities and new project development across the globe and drives continuous improvement and advancement of our control, automation, and optimization technologies. Technical leadership and mentoring are key facets of the job.

Job Role Responsibilities

• Join and contribute to the efforts of a highly driven team of application engineers working towards autonomous operation of ExxonMobil's manufacturing facilities
• Support research and development activities to advance the tools and technology effectiveness including the use of modern techniques such as machine learning and artificial intelligence; interface with other R&D organizations within the Company to identify opportunities and pilot and deploy new technologies
• Work collaboratively with vendors and suppliers to ensure appropriate design, manufacture, testing, and delivery of software; engage with industry and academia for broader awareness of emerging trends and to influence research and product development
• Provide engineering support for our world-wide affiliates (Upstream, Downstream, Chemicals, and Low Carbon Solutions) to develop, deploy, and sustain our process control and optimization technologies
• Support technologies ranging from basic regulatory control, state-based and sequential control, human-machine interfaces, abnormal event detection and operator advisory, to dynamic and steady-state simulation, linear and nonlinear model predictive control (MPC), and real time optimization (RTO)
• Work closely with manufacturing site personnel, process engineering, and the business stakeholders to identify opportunities and define scope, cost, benefits, and project schedule for both new and revamped applications
• Provide active technical leadership through mentoring, proactively sharing technical knowledge, influencing across organizational boundaries, implementing improvements, and delivering value capture

Expected Level of Proficiency

• PhD degree in Chemical Engineering is preferred, a PhD degree from another engineering or computational data science discipline is acceptable if the applicant can demonstrate a solid understanding of control theory, nonlinear mathematical optimization, as well as chemical and refining unit operations
• Ability to understand and model chemical engineering unit operations
• Working knowledge of commercial modeling packages (gPROMS or Aspen Plus, etc.) is advantageous
• Demonstrated skills in modern programming tools / languages (Matlab, Python, IDAES, etc.) and/or data analytics and visualization tools (SeeQ, JMP, Tableau, PowerBI) are required
• Practical experience through internships or industrially focused project is preferred
• Familiarity with an industrial distributed control system is advantageous
• Familiarity with software platforms used for advanced control, modeling or optimization is advantageous. Examples include: multivariable model predictive control (AspenTech DMC, Honeywell Profit Controller, Rockwell Automation Pavilion8, or equivalent), real-time optimization (AVEVA ROMeo, Honeywell Profit Optimizer), or software used for open equation modeling or non-linear programming / control (gPROMS)
• In addition to strong technical background in advanced process control and optimization, successful candidates will demonstrate innovative thinking, strong initiative, and exemplary communication skills with the ability to interact effectively with peers, across organizational boundaries, and in diverse and globally dispersed teams
• Demonstrated ability as a formal or informal leader and mentor
• Willingness to travel domestically or internationally as needed (between 5-40%)