Join the energy transition at DIFFER!At the Dutch Institute for Fundamental Energy Research (DIFFER), we explore cutting-edge solutions for a sustainable future. We are looking for a motivated PhD candidate to join our international team and contribute to pioneering research.
Status
open
Location
De Zaale 20 Eindhoven
Department
Theme Solar Fuels
Group
Autonomous Energy Materials Discovery
Kind of contract
Temporary
Kind of function
PhD student
Working hours

POSITION AND REQUIREMENTS

Responsibilities:
1. Design and implement data pipelines to transform experimental outputs into structured, machine-learning-ready formats using standardized schemas and metadata models.
2. Facilitate the use of structured data by machine learning tools through appropriate access and formatting strategies.
3. Explore basic machine learning tasks such as trend detection, clustering, or dimensionality reduction to assess data quality and infrastructure readiness.
4. Collaborate with researchers in the SDL consortium to align infrastructure design with experimental workflows and project objectives.
5. Supervise BSc/MSc student projects when appropriate.
6. Contribute to scientific dissemination, including research publications, presentations at conferences, and stakeholder meetings.
7. Complete a PhD thesis based on the research within four years.
Requirements:
1. A Master’s degree in computer science, data science, artificial intelligence, or a related field with a strong focus on data engineering or applied machine learning.
2. Experience with data structuring, transformation, and pipeline development, including database design (SQL/NoSQL), data preprocessing, and data integration.
3. Proficiency in Python programming and familiarity with relevant libraries for data handling and processing (e.g. pandas, NumPy, h5py, xarray).
4. Knowledge of graph databases and their application for managing and querying interconnected datasets.
5. Experience in applying machine learning techniques (e.g. clustering, dimensionality reduction) for insight generation and exploratory data analysis.
6. Awareness of FAIR data principles or experience handling simulation or experimental data in a reproducible and structured way.
7. Good communication skills and the ability to work effectively in a multidisciplinary and collaborative environment.
8. Proficiency in written and spoken English.

Please refer the Job description for details