MSc Internship: Data-driven inverse problem of gravimetric data with neural learning in geophysical exploration

MSc Internship: Data-driven inverse problem of gravimetric data with neural learning in geophysical exploration Download job offer in PDF format Contract type : Internship agreement Level of qualifications required : Master's or equivalent Fonction : Internship Research Context Within a collaboration between Inria Team Makutu and Orano, we propose a 6-month MSc internship on inverse problems in Earth imaging, with a focus on approaches based on neural models. Gravity surveys provide a means of characterizing the subsurface by detecting variations in the Earth’s gravitational field arising from contrasts in rock density. Subsurface density contrasts can be associated with hydrothermal alteration, and are employed as indicators of geological environment hosting a mineral of interest. In this internship, we consider the inverse problem of imaging 3D density profiles of the Earth subsurface from ground gravimetric data measuring gravitational acceleration. The internship will focus on machine-learning-based approaches to gravity inversion. In particular, we will investigate both surrogate-based inversion methods, in which neural models are used to approximate the forward or inverse mapping, and direct neural approaches that learn an approximation of the inverse mapping from gravity observations to subsurface density models. Depending on the work progress, complementary data-sets, such as electromagnetic data, may be considered. Assignment In a data- and machine-learning-driven perspective, we aim to develop an in-house computational framework for neural models to image subsurface density distributions from gravity data. The choice of methodology and neural architectures (Fourier Neural Operator, deep operator, convolutional network, etc.) will be guided by discretization invariance and flexibility with respect to the observable inputs and functional outputs. Computational cost and implementation complexity must also be controlled to enable scalability to realistic acquisition surveys. Main activities The research activities will explore the different steps of the inverse problem and its resolution with neural models. 1. Familiarize with the mathematical modeling of gravimetric data, as well as their characteristics in realistic surveys, e.g., the equations involved, the spatial configurations of receiver networks, and processed signals used for imaging. 2. Familiarize with existing neural architectures for forward and inverse (density imaging from gravimetric data) problems. 3. Create catalogs of training data with high-fidelity solvers. First, consider density profiles representative of geological host environments, in discussion with Orano. We will create a control group comprising profiles that are typically not candidates, as well as profiles that may lead to false predictions. Second, we compute noisy observables associated with the selected profiles using the Poisson kernel, with various types of noise added. 4. Consider at least two neural architectures and compare them in terms of training cost, out-of-distribution performance, robustness to noise, and scalability. Benefits package • Subsidized meals • Partial reimbursement of public transport costs • Possibility of teleworking and flexible organization of working hours • Professional equipment available (videoconferencing, loan of computer equipment, etc.) • Social, cultural and sports events and activities • Access to vocational training • Social security coverage Remuneration Internship allowance Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Earth, Environmental and Energy Sciences Scientific computing (BAP E) • Town/city : Pau • Inria Center : Centre Inria de l'université de Bordeaux • Starting date : 2027-03-01 • Duration of contract : 7 months • Deadline to apply : 2026-12-03 Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. Instruction to apply Please send your CV and covering letter Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities. Contacts • Inria Team : MAKUTU • Recruiter : Faucher Florian / florian.faucher@inria.fr The keys to success The applicant should have a strong background in applied mathematics and computer sciences. In addition to research and software development, the applicant will be expected to actively contribute to the team’s activities. The documentation of research results will also be important. About Inria Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.