R&T Associate in Environmental Informatics (M/F)
The successful candidate will join the e-Science Unit of the “Environmental Research and Innovation” (ERIN) department. With a team of more than 170 scientists and engineers from life science, environmental science, and IT science, the ERIN department tackles major environmental challenges our society is facing today such as climate change mitigation, ecosystem resilience, sustainable energy systems, efficient use of renewable resources, environmental pollution prevention and control.
The e-Science Unit of the Environmental Research and Innovation department (ERIN) of LIST is designing, implementing and evaluating innovative ICT methods and applications required in the environmental domain for sustainable resources management, environmental impact reduction and disaster management..
In particular, the Unit is focusing on Data Analytics (e.g. Artificial Intelligence, Machine Learning and Statistical Analysis) and on 2D/3D Interactive Visualisation (e.g. Visual Analytics, Augmented Reality and Geographical Information Systems).
The e-Science Unit cooperates with various experts from environmental sciences in order to develop new algorithms, create effective methods and to build efficient software applications. The unit's work is designed to assist domain experts in gaining insights into complex structured and unstructured data. Work also extends to predicting the expected behaviour of systems. Some recent examples include: biological pathway visualisation, visual analytics of high-dimensional data in “omics” sciences, forecasting the energy production of photovoltaic panels in urban areas, machine learning using satellite image processing and neural networks for sensor data analytics in hydrology.
The proposed position aims to extent the existing expertise in data analytics applied to environmental applications.
We are looking for an R&T Associate who will:
- Contribute to a stream of activities on the use of data processing techniques (e.g. Artificial Intelligence, Machine Learning and Statistics)
- Deal with (large) datasets produced by (high-throughput) devices or sensors with a specific focus on environment relevant issues. In this regard, specific knowledge and experience in handling large amounts of data (> 1 TB) are highly appreciated. Some experience in 2D / 3D visualisation is not mandatory but will be considered as an advantage
- Define parts of RDI projects relying on her/his state of the art knowledge and know-how in terms of data analytics
- Formulate innovative/scientific/technological concepts
- Produce deliverables in given timeframes
The selected candidate will be asked to apply his/her skills to research projects as well as to collaborative projects with industry.
- The position requires a strong interest and proven experience in research (e.g. accepted publications) as well as in software development
- Some experience in contributing to writing project proposals which target national or international calls is considered an asset
- International experience is considered an asset
- A PhD in computer science
- Additional diploma/qualification in relation with environmental sciences, biology, life sciences or bioengineering is considered an asset
- Minimum 4-year RDI experience after the completion of PhD
- Knowledge of Luxembourg and the Luxembourgish socio-economic and industrial context is considered an asset
- Artificial Intelligence, Machine Learning, Statistics, Time-Series Analytics, Signal Processing (theoretical knowledge and experience in related software tools and libraries)
- Software engineering (methods and programming languages, like Java, C/C++, Python, SQL)
- Interdisciplinary thinking, well-developed communication skills (oral and written) and team spirit to successfully integrate into the multicultural, multilingual and multidisciplinary work environment at LIST
- Strongly motivated, challenge and result oriented mind-set
- English mandatory
- French and/or German are considered an asset
This job comes from a partnership with Science Magazine and