Data Scientist Traineeship Opportunity (Erasmus+ Candidates Only)
Engineering and/or Technology, Mathematics and/or Informatics, Natural Sciences
This is a Digital Opportunities Traineeship (DOT). If you want to apply for this internship, please remember that you have to be a student or recently graduated based in one of the 33 Programme Countries participating in Erasmus+ or the Horizon 2020 Associated Countries.
Before applying for a Digital Opportunity Traineeship, we encourage you to check with your university if you are eligible for Erasmus+ traineeship. You can read more about DOT's in our information page.
General Information
Duration: 6 months
Commitment: Full-time
Description
We are seeking a highly skilled and autonomous Data Scientist trainee for a minimum 6-month engagement within the Plantiverse project. This role focuses on developing and optimizing data-driven models to support AI/ML-based agricultural systems. The candidate will work in a collaborative environment but must be capable of handling independent tasks with minimal supervision.
Ideal Candidate Profile
Educational Background:
* Enrolled in or recently graduated from a program in Data Science, Computer Science, Applied Mathematics, or a related quantitative field.
Technical Skills:
* Strong proficiency in Python, R, or similar programming languages, with a focus on data analysis and machine learning applications.
* Experience with large datasets, statistical methods, and applying machine learning models.
* Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) and libraries such as Scikit-learn.
* Practical experience in data preprocessing, feature engineering, and model deployment.
* Understanding of cloud platforms (e.g., AWS, Azure) for data processing and training.
Language Requirement:
* A good command of English is essential for communication, collaboration, and documentation.
Learning and Development:
* Demonstrated interest in applying data science to real-world agricultural challenges.
* Ability to independently develop, test, and refine predictive models.
Collaboration and Communication:
* Excellent written and verbal communication skills to report findings and collaborate effectively with team members.
* Leadership potential to guide other trainees, if necessary.
Daily Tasks:
* Develop and implement machine learning models to optimize resource management in agriculture (e.g., irrigation, pest control).
* Analyze sensor and satellite data to derive insights on plant health and environmental conditions.
* Collaborate with AI/ML teams to integrate predictive models into the Plantiverse platform.
* Regularly assess model performance and refine parameters for optimal accuracy.
* Document processes and findings to support the broader team and future project iterations.
Additional Information: This traineeship offers the opportunity to lead the data-driven aspects of a pioneering plant-driven agricultural platform. It requires strong technical skills and independence in managing projects. Partial online participation is possible to accommodate academic needs.
How to Apply: Candidates interested in applying for this competitive traineeship should submit their CV, Cover Letter, and indicate their period of interest. Applications must be titled:
“Data_Scientist_Name_Surname_ddmmyy_Country” and sent to info@ecobubble.it.
Note: We are currently considering Erasmus+ candidates only.
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