About this role
This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. Video: https://youtu.be/Xt-yHCN5QS0 About the position The Department of Geosciences (IGV) has a vacancy for a full-time 100% position as a PhD candidate within the field of Mining Engineering. The prospective candidate will be part of the Mining Engineering research group at IGV but will also collaborate with other NTNU departments and societal stakeholders. Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia. Your immediate leader will be the Head of Department and the main supervisor of the project will be Professor Hakan Basarir. About the project The project focuses on developing an advanced, AI-integrated methodology for adaptive underground stope dimensioning to address the limitations of traditional empirical approaches. While underground stope design is critical for safe and efficient ore extraction, it is often constrained by sparse geotechnical data. Conventional interpolation techniques frequently struggle with site-specific conditions such as structural anisotropy and in-situ stress variations leading to either overly conservative designs or unplanned dilution. To overcome these challenges, this project will embed user-defined conditions and structural constraints directly into machine learning routines to achieve high-resolution 3D interpolation of rock mass properties. These synthesized geotechnical fields will feed into an automated stope design workflow, producing variable-length, locally adaptive geometries that optimize the balance between stability, recovery, and dilution control. Through collaboration with national and international stakeholders, the project aims to establish a validated, data-driven design framework. Key outcomes will include an open-source Python toolkit, measurable improvements in stope performance, and practitioner guidelines for next-generation underground mine planning. The successful candidate will become part of the newly established MIN30 initiative at the NTNU Mineral Centre, a strategic research initiative focused on securing the future supply of critical minerals through sustainable and innovative technologies. The centre covers the entire mineral value chain, including exploration and resource characterization, mineral processing and extraction, by-product utilization, recycling, metal production, and environmental aspects associated with sustainable and responsible resource development. The MIN30 initiative will recruit several PhD candidates working on both fundamental studies and industrial applications related to future mineral and mining technologies and methodologies. The candidate hired in this position will be integrated with other PhD candidates and researchers within the centre. Duties of the position Conduct rock engineering and geological assessments to identify key parameters influencing stope stability and dimensioning. Evaluate traditional empirical design methods and quantify their limitations in heterogeneous or structurally complex rock masses. Collect, preprocess, and synthesize geotechnical data from diverse sources, including underground mining case studies, drill logs, structural mapping, and cavity monitoring (CMS) records. Develop and train user-informed machine learning models that incorporate geological/geotechnical priors to interpolate 3D rock mass properties between sparse data points. Architect and implement a new stope optimization algorithm that interface with interpolated geotechnical fields to generate adaptive, variable-length stope geometries. Calibrate and validate the developed models using real-world mine data Develop, document, and maintain an open-source Python toolkit for adaptive stope dimensioning and geospatial interpolation. Engage with industry partners, academic research groups to ensure practical relevance and facilitate technology transfer. Prepare technical reports, design guidelines, and high-impact peer-reviewed publications. Present research findings at national and international conferences, workshops, and industry stakeholder meetings. Complete the doctoral education until obtaining a doctorate Carry out research of good quality within the framework described above Be prepared for changes to your work duties after employment. Required selection criteria You must have an academically relevant background within Mining engineering, Engineering geology, Rock mechanics or rock engineer, Geotechnical engineering, Computational engineering or applied mathematics, Data science or Artificial intelligence. You must have a Master's degree in above listed areas or equivalent. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained at master's level. Strong analytical and problem-solving skills with the ability to work independently and collaboratively in interdisciplinary research environments. You must have a strong academic background from your previous studies and have an average grade from your Master's degree study, or equivalent education, which is equal to B or better compared to NTNU's grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker grade background, you may be considered if you can document that you are particularly suitable for a PhD education. Experience with programming (ideally Python) and a foundational understandi