Professor Information Systems Engineering and Management

About ISEM at Harrisburg University:

Harrisburg University of Science and Technology's mission is to stimulate economic growth in our region and beyond through an applied science and technology education that gives students a clear, dedicated pathway into STEM careers.

Within this mission, the Department of Information Systems Engineering and Management (ISEM) has three core concentrations — Information Systems, Systems Engineering, and Operations Management — and addresses problems across services, manufacturing, healthcare, and supply chain-intensive industries. The department offers programs at all three levels: an ISEM PhD and Doctor of Engineering, an ISEM Master's, and a Bachelor of Science in Information Systems and Information Technology.

This search is focused on strengthening the program's depth in supply chain and/or manufacturing, paired with strong, demonstrated expertise in artificial intelligence and machine learning, including the practical use of large language models (LLMs) and agentic AI systems to solve engineering and business problems. The appointment will be at the Assistant or Associate Professor level, with teaching responsibilities at the Master's and Doctoral levels.

We seek a dedicated, research-active individual who is passionate about student success and contributing to a collaborative academic community.

Position Summary:

We have an opening for a full-time faculty position at the Assistant Professor level in ISEM with a specialization in supply chain operations and logistics and/or manufacturing. The successful candidate will bring an excellent academic background together with corporate or industrial experience and will be equally comfortable teaching foundational and advanced AI/ML courses as they are teaching supply chain or manufacturing systems courses. Candidates must be able to introduce students to the major AI/ML model families and show how to apply them — on their own and in conjunction with LLMs and agentic workflows — to real operational and engineering problems. The candidate will teach at both the master’s and PhD levels and is expected to sustain a high-quality, externally visible research agenda.

We are looking for demonstrated depth across some of the following areas:

• Supply Chain Operations, Logistics, and Analytics: A strong, proven background in supply chain and logistics, including demand forecasting, network and inventory design, sales & operations planning (S&OP), procurement and sourcing strategy, transportation and warehouse operations, and end-to-end planning and execution. Experience applying AI/ML and optimization to these problems — e.g., demand sensing, inventory optimization, network design, and supply chain risk and resilience modeling — is required.
• Additive Manufacturing and Advanced Manufacturing Systems: A strong, proven background in additive manufacturing and related advanced manufacturing processes (hybrid manufacturing, precision machining and forming, advanced materials and composites, metrology and inspection, or advanced process planning and control), with the ability to translate industrial practice into graduate-level teaching and research. Candidates whose primary depth is in supply chain, with working familiarity in additive/advanced manufacturing, are equally encouraged to apply, and vice versa.
• Applied AI/ML for Supply Chain and Manufacturing: A demonstrated record of applying AI/ML to real supply chain or manufacturing problems — such as demand forecasting and inventory optimization, machine vision and deep learning for inspection and defect detection, predictive maintenance and remaining useful life estimation, generative design and process parameter optimization, and reinforcement learning for scheduling, routing, or process control. Experience deploying models in production (including MLOps) is valued as evidence of real-world impact.
• AI/ML Foundations and Teaching: A deep understanding of AI/ML theory, concepts, and methods across model families — supervised, unsupervised, and self-supervised learning; deep learning architectures; Bayesian methods, decision trees, and regression — with the ability to teach these foundations at both introductory and advanced levels, and to integrate them with optimization and simulation. Candidates must be prepared to design and teach both foundational and advanced applied AI/ML courses within the ISEM curriculum, not limited to supply chain or manufacturing applications.
• Generative AI, LLMs, and Agentic Systems: Working expertise in large language models, retrieval-augmented generation, and agentic AI architectures, and the ability to teach students how to apply these alongside classical AI/ML and OR models to solve supply chain and manufacturing problems — for example, planning copilots, automated document and report generation, knowledge extraction from operational data, and multi-agent decision support. Attention to responsible use — explainability, validation and monitoring, and data governance — is preferred.
• Smart Systems, Industry 4.0, and Digital Twins: Understanding of cyber-physical systems, IoT-enabled sensing, digital twin development, and the integration of shop-floor or supply chain systems with MES/ERP and enterprise architecture, including turning live operational data into information and knowledge for decision making.
• Supply Chain and Manufacturing Analytics: Expertise in predictive and prescriptive techniques applied to operational systems — statistical process control, discrete-event simulation, scheduling, and optimization/heuristic methods — with the ability to model complex systems and develop resilient, sustainable solutions for real-world settings.
• Blockchain and Distributed Ledger Technologies: Working knowledge of blockchain and distributed ledger applications in supply chain and manufacturing settings, such as provenance and traceability tracking, smart contracts for supplier and logistics agreements, multi-party data sharing and trust across supply chain partners, and integration with IoT and sensor data for verifiable chain-of-custody records. Ability to teach the strengths, limitations, and appropriate use cases of blockchain relative to conventional database and analytics approaches is valued.

Successful candidates will:

• Be skilled teachers, especially in online environments, able to design engaging, up-to-date curricula that integrate theoretical concepts with current academic and industrial developments in supply chain, additive manufacturing, and applied AI.
• Have experience with empirical research design methods — experimental design, longitudinal vs. cross-sectional design, survey design, mixed-method design — used to collect, analyze, and interpret data to answer research questions or test hypotheses.
• Have an established or emerging research agenda in supply chain, additive/advanced manufacturing, applied AI, or their intersection, with publication in reputable peer-reviewed journals and industry outlets.
• Have expertise or a track record in securing research grants and funding from government agencies (e.g., NSF, DOE, NIST/MEP, DoD, manufacturing innovation institutes), private organizations, and industry partners.
• Collaborate with colleagues within and outside the institution to advance interdisciplinary research and educational initiatives at the intersection of supply chain, manufacturing technology, data, and AI.
• Build and maintain partnerships with industry, manufacturers, government bodies, and relevant organizations to drive innovation and knowledge exchange.
• Supervise and mentor graduate students, guiding their research projects and career development.
• Have hands-on familiarity with supply chain or manufacturing lab/pilot-scale environments (e.g., additive manufacturing, automation cells, metrology, sensor instrumentation) and an interest in helping build and sustain such capabilities.

Essential Responsibilities: 

• Commitment to working with diverse student populations at both Master's and PhD levels, with a duty to equity in education.
• Commitment to delivering quality education per HU and ISEM requirements, and to developing, managing, and continuously improving program curricula.
• Commitment to building a solid research agenda, securing grants and funding, and producing peer-reviewed publications.
• Provide academic and student support services to the university community.
• Participate in recruitment activities.
• Manage external industry and institutional partnerships.
• Support the development of supply chain and additive/advanced manufacturing curricula, laboratory capability, and industry-sponsored applied projects, including the integration of AI/ML and agentic tools into coursework.

Qualifications:

Interested candidates should have:

• An earned PhD or equivalent in Industrial Engineering, Supply Chain Management, Manufacturing, Mechanical, or Systems Engineering, or a closely related engineering or management discipline (required).
• A strong, proven background in supply chain operations/logistics and/or additive/advanced manufacturing, gained through industry practice, applied research, or both (required).
• Demonstrated experience applying artificial intelligence and machine learning — including LLMs and agentic AI — to supply chain or manufacturing problems, evidenced by deployed solutions, applied research, patents, or publications (required).
• Demonstrated readiness to design and teach foundational and advanced applied AI/ML courses, in addition to supply chain and/or manufacturing courses (required).
• Demonstrated teaching experience at the graduate and/or doctoral level.
• A research track record with scholarly outputs — conference presentations, refereed journal publications, and other scholarly works — is preferred.
• Willingness and aptitude for teaching and using technology in the classroom.
• Experience working with, mentoring, and advising students.
• Current or prior corporate/industry experience in supply chain, logistics, or manufacturing settings is preferred and valued.

In addition to the required qualifications above, ideal candidates will have experience with learning management systems (LMS) and online teaching platforms. The required HU LMS technology is Canvas, with MS Teams for collaboration.

To Apply 

Interested candidates should apply on the HU website. Candidates will be evaluated based on their skills and expertise to best complement the current ISEM faculty. Specific questions can be forwarded to the Office of Human Resources at HR@HarrisburgU.edu.