Case Western Reserve University

Position Title: Postdoctoral Researcher – Potash brine and phosphogypsum valorization. Duration: 12 months. Position Summary: We are seeking a highly motivated and technically skilled postdoctoral researcher to join a multidisciplinary research project dedicated to the sustainable valorization of potash brine and phosphogypsum, with a specific focus on bioleaching technologies. The successful candidate will contribute to advancing the use of environmentally friendly bioleaching techniques for the extraction of valuable resources from these industrial by-products. The project embedded in a circular economy approach aimed at reducing environmental impacts while creating value from industrial co-products, namely phosphogypsum and residual brines generated by sedimentary potash processing activities. The postdoctoral researcher will investigate the valorization of each material separately through physicochemical and biological approaches, with the objective of identifying optimal pathways for resource recovery. The project will also focus on the combined treatment of phosphogypsum and brines, studying the synergistic and symbiotic effects arising from their interaction within integrated bioprocesses. This work will contribute to the development of innovative and sustainable strategies for the recovery of sulfur, magnesium, potassium and other valuable elements from industrial residues. Area of Research: Valorization of industrial by-products derived from sedimentary potash processing and phosphoric acid production. The research will involve physicochemical and biological treatment approaches for potash brines and phosphogypsum, as well as the development of integrated processes exploiting potential synergistic effects between both materials within a circular economy framework. Main responsibilities: Conduct a comprehensive literature review on potash brine management, phosphogypsum valorization, and related physicochemical and biological processing routes. Perform chemical, mineralogical, and physicochemical characterization of potash brines and phosphogypsum. Design and carry out laboratory-scale experiments to investigate separate valorization pathways for brines and phosphogypsum; Develop and evaluate integrated treatment strategies combining both materials, with particular attention to synergistic and symbiotic effects; Study process parameters affecting the recovery of sulfur, magnesium, potassium, and other valuable elements; Analyze, interpret, and synthesize experimental results to support process optimization; Contribute to the assessment of the environmental and sustainability performance of the developed processes; Prepare scientific publications, technical reports, and presentations; Candidate Profile: Required qualifications : PhD in chemical Engineering, biotechnology or related discipline. Proven experience in mineral characterization, liquid analysis and process development; Strong background in solution chemistry and bioprocessing; Excellent analytical and problem-solving skills; Strong scientific writing and communication abilities in English; Ability to work both independently and within a collaborative research environment. Application Documents: Interested candidates should submit the following documents: Cover letter Detailed CV List of publications Copies of diplomas Professional references (minimum of 2) 

Part-time (0.5 FTE) About the National Art School Our campus is located on Gadigal land in central Sydney, within the historic sandstone walls of what was once the Darlinghurst Gaol and is now one of the nation’s premier art institutions where many of Australia’s leading artists having studied and taught. The National Art School (NAS) is Australia’s leading independent art school, dedicated to exceptional, studio-based art education and shaping contemporary art practice.  NAS is accredited through TEQSA and offers Bachelor of Fine Art, Bachelor of Fine Art (Honours), Masters and Doctorate courses. The School is at the forefront of learning, creativity and innovative practice utilising its independent identity and distinctive studio-based teaching model to deliver exceptional higher education and research, scholarship and professional practice. The position The Lecturer – Photomedia is primarily responsible for teaching and supervision at all levels within the degree programs of the National Art School (NAS), including the development of appropriate teaching resources and related course administration within the Photomedia department. The role also requires a broad contribution to NAS operations including participation in School committees, events, student recruitment and promotional activities. Academic faculty employed at this level are expected to be professionally active within their field of specialisation and are expected to make a significant contribution to practice, scholarship and teaching in their discipline at a national level. What you bring to the role Postgraduate qualifications at AQF Level 10, or equivalent professional experience, are required. A strong record and current profile of artistic practice in contemporary art within Photomedia is essential, demonstrated through exhibitions, scholarly activity, or national‑level publication. The role requires a deep understanding of the expanded field of Photomedia, encompassing still and moving image practices, analogue and digital processes, and interdisciplinary forms. Current working knowledge of industry‑standard software and digital workflows is expected, along with the ability to apply these effectively in studio teaching, curriculum delivery, and student support. The position also calls for the capability to embed historical, theoretical, and critical context within studio‑based learning. How to Apply Please visit the National Art School website www.nas.edu.au for more information and to download the position description. Please include the following in your application: A covering letter addressing how your experience matches the job requirements. A comprehensive resume citing your academic qualifications and outlining your teaching, research and professional experience. If you have any questions or if we can assist in making the application process more accessible for you, please call Juanita Hyde on 02 9339 8672 or email Juanita.hyde@nas.edu.au. Please email your application to hr@nas.edu.au The National Art School is an equal opportunity employer and is committed to workplace diversity. People from different cultural backgrounds are encouraged to apply, including Aboriginal and Torres Strait Islander people and culturally and linguistically diverse people. A competitive remuneration package is available to the successful candidate. The closing date for all applications will be 10 May 2026.

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.  The Research Engineer will play a key role in automated wildlife identification and classification from trap camera images using cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap camera footage for wildlife monitoring and conservation efforts. Job Responsibilities Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate the models into a working system prototype. Perform data annotation. Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency. Prepare project documentation, technical reports, and academic publications. Collaborate with industry partners and contribute to technology transfer efforts. Support the design of simple web interfaces or dashboards to visualise CV model outputs, working alongside developers when needed. Contribute to system integration by applying familiarity with backend/frontend workflows, ensuring CV models can be accessed through user-facing applications. Assist in deployment of CV solutions on cloud or edge platforms with basic interface support for end-users. The candidate is to liaise and communicate with any internal or external stakeholders to ensure project deliverables are met and to perform any other adhoc duties assigned by Supervisor.  Technical Requirements: Possess strong technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow, Keras) Computer vision models for object detection and classification (e.g., YOLO, R-CNN variants, EfficientNet, ResNet, U-Net) Image processing and computer vision techniques  Python programming and relevant libraries (e.g., OpenCV, NumPy, scikit-learn, Pandas, Matplotlib) Experience with dataset preparation, model training, and performance evaluation Candidates with strong computer vision expertise and proven success in 1-2 substantial CV projects are welcome to apply regardless of domain-specific experience Familiarity with Web/Full-Stack Development: Basic understanding of frontend frameworks (e.g., React, Angular, or Vue.js) Eposure to backend development (e.g., Flask, Django, Node.js) Awareness of RESTful APIs and microservices architecture. General knowledge of database systems (SQL/NoSQL). Experience with cloud platforms (AWS, GCP, Azure) for deployment and scaling Educational Requirements: Hold at least a Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological datasets or wildlife imagery Familiarity with data annotation tools and practices for large-scale datasets Knowledge of model deployment and optimization (e.g., ONNX, TensorRT, model quantization) Experience with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices