University of Nevada, Reno

Abstract: This position focuses on the development of intelligent and autonomous drone systems, integrating AI, edge computing, and digital twin technologies for mission autonomy and predictive analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission management platforms, contributing to the deployment of smart UAV demonstrators under industrial conditions. Keywords: Artificial intelligence, autonomy, digital twin, edge computing, UAV systems. Objectives: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration and real-time data acquisition systems. Participate in testing and deployment of UAV demonstrators at UM6P and OCP sites. Methodology: AI Development: Reinforcement learning, computer vision, and sensor fusion for autonomy. Digital Twin & Edge Computing: Implement real-time synchronization and analytics. IoT Integration: Design and manage data acquisition from sensors and UAVs via embedded systems. Validation: Participate in field trials for performance and reliability testing. Expected Outcomes: Contribution to AI-enabled UAV demonstrator (TRL 6). Support in the deployment of a digital mission platform. Participation in academic or industrial publications and reports. Profile of the Research Assistant Candidate & Application Materials: Engineering degree or equivalent in Computer Science, Robotics, or Electrical Engineering. Solid knowledge of AI/ML, embedded systems, and IoT architecture. Programming skills in Python, ROS, TensorFlow, or MATLAB. Strong interest in applied AI and autonomous systems development. Application materials: CV, cover letter, and one recommendation letter.

Position Title: Postdoctoral researcher – Plant ecology and soil–plant interactions in vegetated photovoltaic systems-in mining site. Duration: 12 months, renewable. Position Summary: We are seeking a postdoctoral researcher to join our team and contribute to an innovative research project. This position is part of a project aimed at evaluating the integration of native vegetation beneath photovoltaic installations in semi-arid post-mine environments. The objectives include soil stabilization, dust reduction, and the creation of a favorable microclimate to enhance the energy performance of solar panels. This position offers an excellent opportunity to develop applied research skills in a field with significant environmental impact. Responsibilities: Selection and characterization of native and local plant species (grasses, legumes, pollinator-friendly plants). Implementation of germination, growth, and adaptation trials under semi-arid conditions. Execution of field trials on pilot sites. Analysis of vegetation effects on microclimate. Participation in the analysis of soil–plant interactions (composition, moisture, microbiology). Scientific valorization: publications, reports, and communication of results. Ensure strict adherence to health, safety, and environmental protection regulations in accordance with UM6P standards. Candidate Profile: PhD in plant ecology, environmental sciences, or a related field. Experience in phytomanagement, plant ecology and biology, revegetation of degraded sites. Knowledge of moroccan or mediterranean native plants. Skills in plant propagation and development. Proficiency in biodiversity monitoring tools and plant growth tracking. Field experimentation skills and experience in ecological data collection and analysis. Demonstrated hands-on experience using a variety of laboratory equipment and techniques. Mastery of lab procedures and instruments essential for conducting effective experiments and research. Strong teamwork and collaborative problem-solving skills. Excellent written and oral communication skills in both English and French. Additional Skills (Preferred): Previous post-doctoral experience in plant ecology. Experience with environmental data analysis (R, multivariate statistics). Experience in GIS. Strong scientific writing skills and command of scientific English. Initiative and autonomy to conduct fieldwork. Application Documents: Cover letter Detailed CV List of publications Copies of diplomas Professional references (minimum of 2)

Job Title: Postdoctoral Researcher – AI/ML for Energy Systems Location: Ben Guerir Position Type: Postdoctoral Research Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields. The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa. Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally. The Green Tech Institute at Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco, is seeking a highly motivated postdoctoral researcher to join our team in exploring the intersection of energy transition, circular economy, and sustainable construction production. This position aims to advance innovative solutions that promote sustainability in the built environment, focusing on the effective integration of renewable energy, resource efficiency, and waste reduction. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected to have hands-on experience in field related to urban or rural planning, renewable energy and sustainable construction. Position Overview: We are seeking a talented and motivated Postdoctoral Researcher to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics for real-time battery health monitoring and fault detection. Collaborate with embedded systems and hardware engineering teams to integrate AI models into the BMS. Optimize AI/ML pipelines for resource-constrained environments, including edge AI applications. Guide PhD students and collaborate with engineers to implement advanced ideas. Author scientific publications, present findings at conferences, and contribute to patents or technical innovations. Qualifications: PhD in Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field. Strong experience in developing and applying AI/ML models to energy systems or similar applications. Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Hands-on expertise in programming languages such as Python, R, or MATLAB. Solid understanding of battery systems, electrochemical processes, or energy management. Preferred Skills: Experience with time-series analysis, predictive modeling, and anomaly detection. Familiarity with real-time applications of AI/ML in embedded or IoT devices. Knowledge of cloud-based computing platforms for data processing (e.g., AWS, Google Cloud) Understanding of BMS architecture and electric mobility systems.