CRSA - Assistant Professor in Next-Generation Approaches of Remote Sensing for Applications

Assist. Prof in Next-Generation Approaches of Remote Sensing for Applications to Digital Agriculture

Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an institution oriented towards applied research and innovation. On a specific focus on Africa, UM6P aims to position these fields as the forefront and become a university of international standing.

More than just a traditional academic institution, UM6P is a platform for experimentation and a pool of opportunities, for students, professors and staff. It offers a high-quality living and study environment thanks to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model.

In its research approach, the UM6P promotes transdisciplinary, entrepreneurship spirit and collaboration with external institutions for developing up to date science and at continent level in order to address real challenges.

All our programs run as start-ups and can be self-organized when they reach a critical mass. Thus, academic liberty is promoted as far as funding is developed by research teams.

About CRSA:

CRSA is a main pillar or the newly established college of Sustainable Agriculture and Environmental Science au UM6P. CRSA is a transversal structure across several UM6P Programs and a main pillar of their convergent science research paradigm. Research within the center is organized around several major areas that aim to ensure the challenging food security/sustainable agriculture, water and environmental goal in Africa, with a special focus on developing actionable and impactful methods/tools that use multi-source remotely sensed data. The research aims to improve our understanding of the integrated functioning of continental surfaces and their interaction with climate and humans, with emphasis on sustainable management of natural resources (soil, land, water, agriculture) in the context of Climate Change. The center’s goals is to provide a set of solutions and services via operational products to end- users, key players and main stakeholders (local, national and international) that aid in the decision support and streamline real change and enhancement of water and food systems in Africa.

As the trend in Earth Observation (EO) data stream requires new-generation approaches for the estimation of important soil variables and plants BVs, CRSA is looking to strengthen its team and fill up an academic position in next-generation approaches of remote sensing applied, mainly, to digital agriculture applications and using, various EO datasets with especial emphasise on hyperspectral Data. The new team player is expected to play major role to:

• Combine AI, hyperspectral EO imagery and spectroscopy in Crop Modeling to predict crop growth and yield,
• Develop hybrid approaches combining radiative transfer model and Next-Generation Approaches of Remote Sensing such as machine learning (ML) regression algorithms for timely and accurate estimation of important and plants Biophysical Variables, Train, test and use, inter alia, Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest Regressor (RFR), and Support Vector Regressor (SVR) using (e.g. Colab) for crop modeling,
• Developing, testing deep learning convolutional neural network model to customize products from hyperspectral EO data to model nutrient and carbon fluxes in terrestrial ecosystems,
• To tailor scalable impactful solutions in the data value chain to enrich the crop information environment.

The CRSA invites applications for this position, starting, at the rank of Assistant Professor in any discipline of AI based Next-Generation Approaches of Remote Sensing. We seek a candidate with a robust and advanced academic background in digital image processing, image enhancement, computer vision, pattern recognition, machine learning and deep neural networks and a sound research experience/knowledge in electromagnetic sensing of the Environment and Remote Sensing of Environmental Dynamics.

This position will contribute to research, teaching, community service and outreached functions grounded in the CRSA's ongoing broad topical areas of interest such as:

• Land use/land cover change,
• Natural resources management,
• Soil cover assessment/mapping,
• Arid & semi-arid soils and environments,
• Soil and environmental health,
• Carbon management and dynamics,
• Agricultural management practices,
• Greenhouse gas and climate change mitigation and adaptation,
• Water management.

The successful candidate should combine independence, ambition and team work in the front line of research fields with a strong focus on innovation in both academic and applied research, in close collaboration with international academic and institutional partners.

Key duties:

The specific responsibilities and tasks of the successful candidate will include:

• Scientific outcomes from various research projects,
• Development of research proposals,
• Grants and contracts managing with various research entities,
• Coauthoring manuscripts and scientific reports,
• Supervising graduate students, and disseminating research outcomes to stakeholders,

More specifically, the successful candidate is expected to have the capacity to:

• Develop and lead CRSA research segment on AI applications into Remote Sensing
• Conduct scientific research on hyperspectral EO data for plants and environmental ecosystems
• Develop, with CRSA team, ambitious interdisciplinary applied research projects using AI and multi-sensor remote sensing,
• Teach undergraduate and graduate students Machine learning and artificial intelligence techniques for Remote Sensing data analysis, feature extraction, and classification.
• Contribute to the development of Next-Generation Approaches of Remote Sensing courses/programs including tailored executive education products.
• Create a vigorous, externally-funded research program on AI Next-Generation Approaches of Remote Sensing for Sustainable Agriculture and Environmental sciences,
• Collaborate with other UM6P researchers/groups (e.g. International Water Research Institute, Geology & Sustainable Mining, Soil and Fertilizer Research in Africa) on multidisciplinary research projects with Remote Sensing and GIS components,
• Present research results at scientific meetings and publishing results in peer-reviewed academic journals,
• Supervise Master-students, PhD students and work closely with Postdoctoral fellows.
• Streamline research outputs into substantial impactful outcomes of applied research to boost CRSA’s / UM6P vision to enable Africa.

Criteria of the candidate:

This position is open to individuals with substantial expertise in the target fields. Previous high impact research and teaching expertise will be an asset. Good oral and writing communication skills in English are a must, and knowledge of French represents a significant benefit. Candidate should have solid soft skills for the success in technological research, especially, adaptability and flexibility skills as CRSA researchers navigate complex research situations, collaborate effectively with others, and communicate their findings and ideas effectively to a wide range of stakeholders.

General Criteria :

• PhD degree from a recognized University in Computer Science, Quantitative Remote Sensing, Geomatics or related subjects,
• Postdoctoral research experience in a field related to the duties of the position. AI and Hyperspectral sensors use for Environmental Dynamics characterization of plants BV attribute would be an asset,
• Experience in leading and conducting scientific research projects in AI Digital Image Processing, remote sensing applications to multidisciplinary disciplines (e.g. agriculture/forestry soils),
• Ability to generate new ideas, links, and to build upon existing models to develop novel approaches in natural resources monitoring for soil and vegetation, among others,
• Experience in collaborative research with university groups, international institutions, government labs and industrial partners,
• Experience in undergraduate, graduate, and postdoctoral fellow supervisions

Specific technical Criteria:

• Strong programming skills in Python and R and experience with deep learning frameworks such as TensorFlow, Keras, PyTorch, etc.
• Mastering neural networks and deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders.
• Experience with data pre-processing, feature extraction, and data augmentation techniques.
• Familiarity with cloud computing platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
• Knowledge of big data tools such as Apache Hadoop, Apache Spark, and Apache Cassandra.
• Mastering computer vision and image processing techniques, such as object detection, semantic segmentation, and image classification.
• Understanding of natural language processing (NLP) and its applications, including text classification, sentiment analysis, and machine translation.
• Ability to work with large datasets and perform data analysis using tools such as Pandas, Matplotlib, and Seaborn.
• Familiarity with version control systems such as Git, and experience with agile software development methodologies.

Specific Criteria

• Publication of peer-reviewed articles in the field of AI DIP and remote sensing (at least 10 peer-reviewed papers and first author of a minimum of 7),
• Contributions and presentations in international conferences (at least 15 participations),
• Developing or contributing to research funding proposals (at least 5 proposals).

Skills :

The appointee will be expected to demonstrate competency and relevant skills in the following areas:

Research skills where :

• Using strong programming skills in Python and experience with AI libraries and frameworks such as TensorFlow, Keras, PyTorch, and scikit-learn.
• Demonstarting xperience with remote sensing data processing and analysis, including data pre-processing, feature extraction, and data augmentation techniques.
• Understanding of cloud computing and big data tools, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Apache Hadoop, and ability to leverage these tools for remote sensing data processing and analysis.
• Familiarity with GIS software, such as ArcGIS and QGIS, and experience with spatial data analysis and visualization.
• Familiarity of Remote Sensing commercial software (e.g. ENVI, eCognition) & GIS software (i.e., ESRI ArcGIS) and Opensource SW (QGIS) and Drone Software (e.g. Pix4D, Agisoft),
• Strong analytical and critical thinking skills, including ability to formulate research questions, design experiments, and interpret results.
• Strong problem-solving skills, including ability to identify challenges and develop creative solutions for remote sensing soil and environmental ecosystems applications.
• Strong communication skills, including ability to effectively communicate research results and collaborate with other researchers and stakeholders.
• Strong writing skills, including ability to write clear, concise, and well-structured research papers, proposals, and technical reports.
• Ability to conduct independent research, including ability to design and execute experiments, analyze data, and interpret results.
• Knowledge of research methods, including experimental design, statistical analysis, and model evaluation, and ability to apply these methods to remote sensing applications.
• Familiarity with remote sensing literature and ability to stay current with the latest developments in the field.
• Experience with large datasets and ability to effectively manage and analyze big data.
• Ability to work with cross-disciplinary teams, including ability to collaborate with remote sensing experts, computer scientists, data scientists, and engineers.
• Ability to develop and implement new AI techniques for remote sensing applications.

Teaching skills :

• Evidence of commitment to excellence in teaching,
• Teaching experience in both traditional and alternative (online) learning environments,
• Aptitude for teaching at undergraduate and graduate levels in English,
• Teaching flexibility in a variety of formats (i.e. remote teaching, week-long and semester-long),
• Training of highly qualified personnel including graduate students.

Applications must contain:

• A cover letter indicating the position applied for and the main research interests.
• A detailed CV.
• A brief research statement.
• Contact information of 3 references (applicants are assumed to have obtained their references consent to be contacted for this matter).

The shortlisted candidates will be invited to meet the university selection committee. UM6P.