Postdoctoral Research Associate: AI, Spectroscopy, and Data Science for Agricultural, Food, and Sustainable Systems

The Sensing, AI & Green Systems Engineering (SAGE) Lab at the University of Minnesota Twin Cities is seeking a highly motivated Postdoctoral Research Associate to join our growing interdisciplinary research program.

The SAGE Lab develops data-driven approaches that integrate advanced sensing, spectroscopy, artificial intelligence, machine learning, stochastic modeling and control, and engineering to address challenges across agricultural, food, biological, energy, environmental, and materials systems.

We are particularly interested in candidates with a strong foundation in spectroscopy or spectral imaging, chemometrics, machine learning/deep learning, data science, and scientific computing who are interested in applying these methods to real-world engineering and sustainability challenges.

The successful candidate will have considerable flexibility to contribute ideas, develop new research directions, collaborate across disciplines, and help shape the research portfolio of the SAGE Lab.

Potential Research Areas

Research projects may span several interconnected areas, including:

• NIR, MIR, FTIR, Raman, and related spectroscopic techniques
• Hyperspectral and multispectral imaging
• Spectral preprocessing and chemometrics
• Machine learning and deep learning for spectroscopy, imaging, sensor, and process data
• Multimodal sensing and sensor fusion
• Computer vision and image analysis
• Remote sensing and environmental monitoring
• Precision and digital agriculture
• Crop and plant sensing
• Precision irrigation and nutrient management
• Hydroponic and aquaponic systems
• Controlled-environment agriculture
• Food contamination detection
• Food quality and food-safety monitoring
• AI-enabled food processing and storage
• Biomass characterization and bioenergy systems
• Waste-stream characterization and automated sorting
• Circular-economy and resource-recovery applications
• Materials informatics
• Clean-energy and sustainable processing systems
• Data-driven process monitoring and optimization
• Stochastic modeling and stochastic control
• Decision-making under uncertainty
• Reinforcement learning and sequential decision-making
• Uncertainty quantification
• Uncertainty-aware and trustworthy artificial intelligence
• Transfer learning and domain adaptation
• Machine learning for small, noisy, incomplete, and heterogeneous datasets
• Real-time, automated, and edge-AI sensing systems

The candidate is not expected to have experience in all of these areas. Research directions will depend on the candidate's background, interests, available projects, collaborations, and emerging funding opportunities.

We value individuals who have a strong technical foundation, are intellectually curious, and are willing to learn new experimental and computational methods.

Required Qualifications

Candidates should have:

• A Ph.D. in Agricultural Engineering, Biological/Biosystems Engineering, Food Engineering, Food Science, Chemical Engineering, Electrical Engineering, Computer Engineering, Materials Science and Engineering, Chemistry, Data Science, Computer Science, or a closely related discipline.
• Research experience with spectroscopy, spectral imaging, analytical sensing, chemometrics, or related measurement techniques, together with experience in spectral preprocessing and chemometric analysis, including approaches such as normalization, derivatives, dimensionality reduction, PCA, PLS/PLSR, or related methods.
• Experience applying machine learning or deep learning to spectroscopy, imaging, sensor, experimental, or process data.
• Strong quantitative data-analysis and scientific-programming skills using Python, MATLAB, R, or an equivalent computational platform.
• Demonstrated ability to conduct independent research, analyze experimental data, interpret results, and communicate research findings effectively.
• Strong oral and written scientific communication skills.
• Ability to contribute effectively to peer-reviewed manuscripts, technical documents, presentations, and research proposals.
• Ability to work productively in an interdisciplinary and collaborative research environment.
• Willingness to learn new experimental, computational, analytical, modeling, and proposal-development methods and contribute intellectually to new research directions within the SAGE Lab.

Preferred Qualifications

Experience in one or more of the following areas would be beneficial:

• Hands-on experience with NIR, MIR, FTIR, Raman spectroscopy, hyperspectral imaging, multispectral imaging, or related sensing techniques
• Advanced chemometrics and spectral-data analysis
• Machine learning and deep learning for scientific or engineering applications
• Computer vision and image analysis
• Multimodal sensing and sensor fusion
• Agricultural, food, biological, or environmental systems
• Precision agriculture or controlled-environment agriculture
• Biomass, bioenergy, or biological materials
• Food processing, food quality, or food safety
• Environmental sensing or remote sensing
• Waste-stream characterization and circular systems
• Materials informatics
• Energy and sustainable processing systems
• Uncertainty quantification and uncertainty-aware machine learning
• Stochastic modeling, stochastic control, or optimization under uncertainty
• Reinforcement learning or sequential decision-making
• Model calibration and validation
• Transfer learning and domain adaptation
• Machine learning for limited, noisy, or heterogeneous datasets
• Development of real-time, automated, edge, or deployment-oriented sensing and decision systems
• Experimental design and laboratory or field data collection
• Development of reproducible scientific data and code workflows
• Scientific manuscript preparation
• Competitive research proposal or grant development
• Mentoring undergraduate or graduate researchers

Responsibilities

The postdoctoral researcher will work closely with Dr. Sambandh Dhal and collaborators within and outside the University of Minnesota.

The approximate distribution of responsibilities is:

50% – Research and Technical Development

The postdoctoral researcher will design and conduct interdisciplinary research involving sensing, spectroscopy, data science, artificial intelligence, machine learning, modeling, and engineering.

Activities may include:

• Designing laboratory, field, or computational experiments
• Acquiring, preprocessing, and interpreting spectroscopic and hyperspectral data
• Working with NIR, MIR, FTIR, Raman, hyperspectral imaging, and related sensing platforms
• Developing chemometric and statistical methodologies
• Developing machine-learning and deep-learning models for classification, regression, prediction, anomaly detection, and process monitoring
• Integrating spectral information with RGB/thermal imaging, IoT sensors, environmental measurements, remote-sensing data, and process data
• Developing methodologies for high-dimensional, heterogeneous, noisy, incomplete, or limited datasets
• Developing uncertainty-aware and trustworthy AI methodologies
• Exploring stochastic modeling, stochastic control, reinforcement learning, and decision-making under uncertainty where appropriate
• Evaluating model robustness, uncertainty, calibration, transferability, and generalizability
• Developing transfer-learning and domain-adaptation approaches
• Developing multimodal sensor-fusion methodologies
• Developing data-driven monitoring, optimization, and decision-support systems
• Maintaining reproducible research, data, and code workflows
• Translating research methodologies toward practical, automated, or real-time implementation
• Collaborating with faculty, students, government laboratories, and industry partners

The candidate will be encouraged to take intellectual ownership of research problems and contribute actively to defining new directions rather than only executing predefined tasks.

35% – Proposal Development, Scientific Writing, and Publications

A substantial component of this position will involve working closely with Dr. Dhal to develop the SAGE Lab's externally funded research portfolio and maintain a strong publication program.

Responsibilities may include:

• Identifying relevant federal, state, foundation, and industry funding opportunities
• Conducting targeted literature and programmatic reviews
• Developing new research concepts and funding ideas
• Formulating research questions, hypotheses, objectives, and specific aims
• Developing technical approaches and experimental plans
• Preparing work packages, milestones, timelines, and deliverables
• Generating and analyzing preliminary data
• Preparing proposal figures, schematics, tables, and supporting technical materials
• Drafting and revising scientific and technical sections of competitive proposals
• Integrating contributions from collaborators and external partners
• Assisting with responses to sponsor or reviewer feedback
• Developing collaborative research proposals across disciplines
• Leading and co-authoring peer-reviewed journal publications
• Translating research findings into high-quality scientific manuscripts
• Helping develop publications and preliminary research that strengthen future grant proposals

Strong scientific writing is expected. Previous grant-writing experience is highly desirable, although candidates who are motivated to develop expertise in proposal development are also encouraged to apply.

15% – Collaboration, Mentoring, and Research Dissemination

The postdoctoral researcher will be an active member of the SAGE Lab and the broader research community within the Department of Bioproducts and Biosystems Engineering.

Responsibilities may include:

• Participating in SAGE Lab and departmental research activities
• Collaborating with faculty and external research partners
• Mentoring undergraduate and graduate researchers
• Helping train students in experimental and computational research methods
• Contributing to conference abstracts and presentations
• Presenting research at scientific conferences, seminars, and professional meetings
• Preparing technical reports and other research documentation
• Contributing to invention disclosures and technology-development activities when appropriate
• Supporting a collaborative, interdisciplinary, and inclusive research environment

Why Join the SAGE Lab?

The SAGE Lab is being developed as an interdisciplinary research environment at the intersection of sensing, artificial intelligence, data science, engineering, and sustainability.

This position is intended to provide training not only in conducting high-quality research, but also in developing the broader skills necessary to become an independent researcher.

The postdoctoral researcher will have opportunities to gain experience in:

• Independent research leadership
• Development of new research directions
• Proposal and grant writing
• Scientific publishing
• Experimental and computational research
• Artificial intelligence and data-driven engineering
• Student mentoring
• Interdisciplinary collaboration
• Academic–industry collaboration
• Collaboration with government and national laboratories
• Translation of research into practical technologies
• Building externally funded research programs
• Developing projects from initial concept through proposal, research, publication, and translation

Because the SAGE Lab is a growing research group, the successful candidate will also have the opportunity to contribute meaningfully to the development of its research culture and scientific direction.

Candidates interested in future careers in academia, national laboratories, industry R&D, government research, or interdisciplinary data-driven science and engineering are especially encouraged to apply.

How to Apply

Interested candidates should submit the following materials:

1. Curriculum Vitae
2. Cover letter describing research interests, relevant experience, and motivation for joining the SAGE Lab
3. Brief research statement
4. Contact information for three professional references
5. Up to three representative publications

In your application, please describe your relevant experience in areas such as:

• Spectroscopy, spectral imaging, or sensing
• Chemometrics and spectral-data analysis
• Machine learning, deep learning, or data science
• Scientific programming
• Stochastic modeling, optimization, or control, if applicable
• Agricultural, food, biological, environmental, energy, or materials applications
• Scientific manuscript preparation
• Research proposal or grant development

Candidates whose previous research does not exactly match every research area listed above are still encouraged to apply if they have a strong technical foundation, demonstrated research productivity, intellectual curiosity, and an interest in learning new research areas.

Contact:
Dr. Sambandh Dhal
Assistant Professor
Department of Bioproducts and Biosystems Engineering
University of Minnesota Twin Cities
106 Kaufert Laboratory, St. Paul, MN
Email: sambandh@umn.edu