chemistry $80,000 jobs in Saskatchewan

The University of Regina is a public research university with a strong commitment to interdisciplinary scholarship, graduate education, and research that engages local, national, and global challenges. Located in Regina, Saskatchewan, the University is situated in a mid-sized and increasingly diverse city in the Canadian prairies. The University is situated on Treaty 4 Territory—the territories of the Nêhiyawak, Anihšinapek, Dakota, Lakota, and Nakoda peoples—and the traditional homeland of the Métis/Michif Nation, with a presence in Treaty 6.

Position Overview

The position will focus on designing, developing, and integrating turnkey sensing systems across the full electronics and mechanical lifecycle—from multilayer PCB design and microcontroller programming to graphical user interface (GUI) development and mechanical CAD modelling (AutoCAD, Fusion 360, SolidWorks)—with a strong emphasis on mechatronics and swarm-based unmanned systems. The role involves system-level integration of IoT connectivity, embedded systems, and diverse AI/ML techniques for sensor platforms, alongside the design, prototyping, and field validation of UAVs and drones for land, water, and air operations. The incumbent will contribute to swarm design, including multi-agent coordination, cooperative sensing, communication architectures, formation control, path planning, and decentralized autonomy for UAVs, UGVs, USVs, and hybrid/amphibious platforms. They will also manage project budgets, procurement, vendors, and deliverables for funded research projects, and support field validation, documentation, and training of junior personnel. High-performing candidates may have the opportunity to transition into a job-embedded PhD after 6–12 months, allowing them to pursue doctoral studies while continuing their full-time research and development work at the University.

Responsibilities:

• Design and develop multilayer PCBs for sensor interfacing, signal conditioning, data acquisition, and power management using industry-standard EDA tools (e.g., Altium, KiCad, Eagle).

• Program microcontrollers (ARM Cortex, ESP32, Arduino, PIC, or equivalent) in C/C++, Python, or MicroPython for real-time data acquisition, actuator control, communication protocols, and low-power operation.

• Develop intuitive graphical user interfaces (GUIs) using Qt, PyQt, LabVIEW, or web-based dashboards for real-time data visualization, remote system control, and end-user interaction.

• Lead system-level design integration, ensuring seamless interaction between sensing materials, analog front-ends, digital processing units, communication modules, and power subsystems.

• Create mechanical enclosures, mounting fixtures, and system housings using CAD software, including AutoCAD, Fusion 360, and SolidWorks, to protect electronic assemblies for field-deployable and ruggedized environments.

Implement IoT connectivity (Wi-Fi, Bluetooth, LoRaWAN, Zigbee, cellular, MQTT, HTTP/REST) and integrate AI/ML algorithms for on-device sensor data processing, including:

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  • Edge AI: TensorFlow Lite Micro, CMSIS-NN, or similar frameworks for real-time inference on microcontrollers.
  • Predictive Analytics: Time-series forecasting (LSTM, GRU, Prophet) for predictive maintenance and environmental trend analysis.
  • Anomaly Detection: Isolation Forest, Autoencoders, or One-Class SVM for identifying outliers and sensor faults.
  • Classification: Support Vector Machines (SVM), Random Forest, Gradient Boosting (XGBoost, LightGBM), and Neural Networks for pattern recognition and event detection.
  • Regression Models: Linear/Polynomial Regression, Ridge, Lasso, and Gaussian Process Regression for sensor calibration and concentration prediction.
  • Unsupervised Learning: K-Means, DBSCAN, and PCA for clustering and dimensionality reduction of high-dimensional sensor data.
  • Reinforcement Learning: Application of RL for adaptive sensor control and optimization of system parameters.
  • Deep Learning: CNNs for spectrogram/image analysis and RNNs/LSTMs for sequential data processing.
  • Generative AI: GANs or VAEs for synthetic data generation to augment limited training datasets.

• Manage project budgets, track procurement processes, coordinate with external vendors and subcontractors, and ensure timely delivery of project milestones and deliverables for funded research initiatives.

• Conduct rigorous system testing, validation, troubleshooting, and iterative improvement of turnkey sensing platforms in both laboratory and field settings; document test results and performance metrics.

• Prepare comprehensive technical documentation, including schematics, bills of materials (BOMs), assembly drawings, wiring diagrams, user manuals, and test reports.

• Train and mentor students, junior technicians, and researchers in electronics design, system integration, soldering techniques, and testing procedures.

• Maintain laboratory inventory, electronic components stock, equipment calibration schedules, and safety compliance for electronics prototyping and fabrication workstations.

• Support technology demonstrations for academic researchers, industry representatives, government partners, and prospective commercialization stakeholders.

• Coordinate with external certification bodies and regulatory compliance officers as needed for product safety and electromagnetic compatibility (EMC) testing.

• Participate in out reach programs.

Required Qualifications:

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Mechatronics, Electronics Engineering, Computer Science, or a closely related field.

• Minimum 2–3 years of hands-on experience in PCB design (multilayer, high-speed, mixed-signal), microcontroller programming, and GUI development for hardware systems.

• Proficiency in CAD software including AutoCAD, Fusion 360, and SolidWorks for mechanical design and system integration.

• Demonstrated knowledge of IoT connectivity protocols (e.g., MQTT, HTTP, BLE, LoRa, Wi-Fi) and embedded systems, with practical experience implementing one or more of the following AI/ML techniques:
  • Edge AI frameworks (TensorFlow Lite, CMSIS-NN)
  • Classification and regression models (Random Forest, SVM, XGBoost)
  • Time-series analysis (LSTM, Prophet, ARIMA)
  • Unsupervised learning (PCA, K-Means, Autoencoders)
  • Anomaly detection algorithms

• Proven experience managing project budgets, procurement processes, vendor relationships, and deliverable tracking for funded research or product-development projects.

• Strong troubleshooting, analytical, and documentation skills, with the ability to work independently and collaboratively in a multidisciplinary research environment.

Preferred Qualifications:

• Experience with Altium Designer for high-speed, multilayer PCB layout and signal integrity analysis.

• Familiarity with sensor integration (chemical, gas, environmental, or optical sensors) and analog front-end circuit design (op-amps, filters, ADCs).

• Experience with field deployments of electronic systems in outdoor or industrial environments, including ruggedized packaging and weatherproofing.

• Knowledge of product certification standards (FCC, IC, CE, UL) and quality assurance procedures.

• Experience working in a university research lab, startup, or product-development environment with a track record of translating prototypes to near-market systems.

• Proficiency in version control (Git) and collaborative software development practices.

• Experience with cloud-based AI/ML services (AWS SageMaker, Azure ML, Google Cloud AI) for scalable data processing and model deployment.

• Knowledge of model optimization techniques (pruning, quantization, knowledge distillation) for efficient deployment on resource-constrained edge devices.

• Familiarity with MLOps practices for continuous integration, deployment, and monitoring of machine learning models in production environments.

• Willingness to engage respectfully with Indigenous communities and partners, where relevant, under the guidance of University specialists.

PhD Conversion Pathway:

This position is designed for talented engineers and scientists who wish to combine cutting-edge turnkey systems development with advanced academic training. After an initial 6–12 month probationary period, high-performing candidates will be eligible to transition into a job-embedded PhD program, co-supervised by faculty in Electronic Systems Engineering. The PhD research will build directly on the incumbent’s work in IoT-enabled sensing systems, AI/ML, and PCB/system integration, allowing the candidate to earn a doctoral degree while remaining employed full-time at the University.

Conditions of Employment:

• Legally entitled to work in Canada.

• Able to comply with applicable University policies, research-security requirements, export controls, laboratory safety requirements, and confidentiality obligations.

• Willing and able to undertake fieldwork and work-related travel, as required.

• Must have a valid driver’s license or be willing to obtain one within six months of joining.

Application Materials:

• Cover letter – Highlight your relevant experience and indicate whether you wish to opt in to or opt out of the PhD conversion pathway.

• Curriculum vitae – Include a list of completed projects, publications, technical skills, and laboratory techniques mastered.

• Research statement (1–2 pages) – Describe your past contributions and future research vision in the development of turnkey systems and sensing.

• Names and contact information for two professional referees (at least one of whom is familiar with your hands-on laboratory work).