AGBS - Post-Doctoral Fellow in Bioinformatics Data Analysis and Mining

College of Sustainable Agriculture and Environmental Science (CSAES)

AgroBioSciences program (AgBS)

Position Announcement - Mohammed VI Polytechnic University (UM6P), College of Sustainable Agriculture and Environmental Science (CSAES), AgroBioSciences program (AgBS),

Duration: 3 years

Location: Benguerir – Morocco

Keywords:  

Computer science, Bioinformatics, Data science, Big Data, Machine learning, Deep learning

About UM6P:

Mohammed VI Polytechnic University (UM6P,https://www.um6p.ma/en) is an international higher education institution, established to provide research and innovation at the service of education and development for Morocco and the African continent. It has a state-of-the-art campus at the heart of the Green City of Benguerir, near Marrakesh. UM6P academics and staff enjoy strong research funding, moderate teaching loads, and excellent facilities. Its research approach is transdisciplinary with an emphasis on international collaboration. Many of our research programs run as start-ups. OCP group (http://www.ocpgroup.ma/en/home), a world leader in fertilizer production, is a major starter client for the University providing capital and research funds. In parallel, a new portfolio of clients is growing with the development of an R&D cluster around the University and a growing number of international partnerships.

About the College of Agriculture and Environmental Science (CAES) and AgroBiosciences Program (AgBS) at UM6P:

The College of Sustainable Agriculture and Environmental Science (CSAES) is a component of the Science & Technology pole of Mohammed VI Polytechnic University (UM6P). It constitutes a structure of higher education and practical-based research with a vision of solving real African agriculture challenges leveraging up to date science and technology. Oriented towards Africa, CSAES acts in connection with a wide network of universities and research centers around the continent in order to link real field issues with up to date science. Research on plant nutrition, abiotic stresses resistance and biofortification is part of the AgroBiosciences Program (AgBS) with main focus on Moroccan and African agriculture challenges in particular meeting the food and nutritional security. 

About the PHENO-MA Platform:

The PHENO-MA is an innovative research platform for high-throughput plant phenotyping built and established at the University Mohammed VI Polytechnic (UM6P) in Benguerir – Morocco. It can be used to assess plant responses to nutrient deficiency, drought, high temperature, pests, and diseases, and exploring smart solutions to enhance resistance and boost crop productivity. The platform includes:

• An outdoor system with a (i) a fully automated lysimetric system with 1400 large container (mini-plots) to study water dynamics and drought scenarios simulation under close-to-field conditions, and (ii) a fully autonomous phenotyping robot, Phenomobile.v2+, equipped with a set of sensors (LiDAR, RGB, IR, and Spectrometer) that enable advanced plant measurements, that allow us to assess plant’s water budget in response to abiotic stress and nutrients’ application. 
• An indoor system with (i) the phenotyping machines, "PhenoMerzouga.v2", which is a small-scale lysimetric system for simulating climate change scenarios, and (ii) 150 m² of growth chambers and speed breeding platform to accelerate the genetic gain and germplasm development.

Job description:

We are seeking applications for a Post-Doctoral Fellow in bioinformatics data analysis and mining to support the implementation and the improvement of PHENO-MA phenotyping platform. The successful candidate will be responsible for working with a multidisciplinary team of researchers and engineers to design and develop data-driven and AI-driven solutions by leveraging various sources to help farmers make accurate decisions in order to improve crop yield. The position is part of the Crop Improvement laboratory and PHENO-MA phenotyping platform at UM6P. Good English communication skills; both verbal and written are required.

Main responsibilities:

• Work closely with a multidisciplinary team of researchers and engineers to develop and implement Data-driven and AI-driven solutions based on the collected data from the PHENO-MA plant phenotyping platform.
• Provide dedicated application support to projects through benchmarking, code optimization or code portability, scalability of applications, collaborations for projects requiring refactoring of codes,
• Collect and analyze data from various sources, including weather data, lysimetric robots’ data, and phenomobile.v2+ data, to develop predictive models and decision support tools, 
• Perform cutting edge research tasks in the fields of deep learning-based crop yield prediction, plant growth prediction, deep learning-based genomic prediction.  
• Contribute to the development and implement of a data management and quality control infrastructure/platform to ensure the accuracy and completeness of the collected data.
• Develop and implement data management and quality control procedures to ensure the accuracy and completeness of the collected data.
• Perform exploratory analyses from genomic/phenotype data for trait discovery and germplasm genetic diversity assessment in response to various plant stress conditions, 
• Develop and implement data visualization tools to help users better understand and interpret the data.
• Conduct research on the latest deep learning techniques and identify potential areas of research application in the context of the PHENO-MA project.
• Continuously evaluate the latest packages and frameworks in the ML ecosystem
• Drive clarity and solve ambiguous, challenging business problems using data-driven & AI-driven approaches. Propose and own data analysis (including collecting, modeling, coding, analytics, and experimentation) to drive business insight and facilitate decisions.

Required qualifications

• PhD degree in Computer Science, Bioinformatics, Machine Learning, or equivalent degree
• Advanced Know-How in the fields of Data engineering, Machine Learning and Deep Learning
• Experience in a research environment with a good track record 
• High proficiency in Python and SQL, and ML/DL frameworks such as Tensorflow and PyTorch.
• Experience with exploratory data analysis, Data extraction/ingestion, statistical analysis and hypothesis testing, and model development.
• Understanding and implementation of project management best practices. In particular, the ability to manage multiple projects under strict timelines as well as the ability to work well in a demanding, dynamic environment and meet overall objectives
• Experience with Agile software development.
• Good English with excellent communications skills; able to communicate analytical and technical content in an easy to understand way to both technical and non-technical audiences.
• Intellectual curiosity, along with excellent problem-solving and quantitative skills, including the ability to desegregate issues, identify root causes and recommend solutions.

Preferred qualifications

• Prior exposure to MLOps/DataOps processes and familiarity with associated platforms like MLflow and Airflow is considered an advantageous asset. Proficiency in MLOps implies a grasp of the end-to-end lifecycle management of machine learning models, encompassing model development, testing, deployment, monitoring, and continuous improvement.
• Academic experience in the field of crop improvement, plant phenotyping, precision agriculture or related fields is a plus.
• Experience with deep multimodal learning, computer vision, and advanced computer vision pre-trained models,  
• Ability to customize and modify existing methodologies/loss-functions/algorithms/systems in order to achieve the business-results while managing operational risk.
• Experience with software REST API design.
• Prior exposure to containerization and DevOps methodology. 
• Experience defining, driving, and executing low-level tasks to completion across multiple simultaneous activities.
• Experience writing quality, well-documented and well-tested code.

Employment terms:

The successful candidate will be employed on a competitive salary by Mohammed VI Polytechnic University (UM6P) based in Benguerir, Morocco.

Applications and selection procedure:

Applications must be sent using a single electronic zipped folder with the mention of the job title in the mails subject. The folder must contain:

• Cover letter indicating the position applied for and the main research interests. 
• Detailed CV.
• Contact information of 3 references (Applicants are assumed to have obtained their references’ consent to be contacted for this matter).

Applications are to be submitted directly on the hiring platform.

Contact information: moez.amri@um6p.ma