To strengthen our team in the division S.3 "eScience" in Berlin-Steglitz, starting as soon as possible, we are looking for a

The Bundesanstalt für Materialforschung und -prüfung (BAM) is a materials research organization in Germany. Our mission is to ensure safety in technology and chemistry. We perform research and testing in materials science, materials engineering and chemistry to improve the safety of products and processes. At BAM we do research that matters. Our work covers a broad array of topics in the focus areas of energy, infrastructure, environment, materials, and analytical sciences.

You will be part of BAM's interdisciplinary research environment and will work with 47 partners from fourteen European countries and Taiwan in the EU Horizon 2020 project "TREEADS – A Holistic Fire Management Ecosystem for Prevention, Detection and Restoration of Environmental Disasters" (https://treeads-project.eu). As part of the EU Green Deal concept, TREEADS develops efficient fire protection and sustainable forest management concepts to react to the consequences of climate change and the associated increasing risk of forest fires. Your task will be the development and implementation of machine learning methods for the prediction and early detection of forest fires, as well as the prediction of fire, heat, and smoke development. To do this, you collect appropriate data from geodatabases, including environmental factors and anthropogenic influences, and ideally also from experiments and simulations. This data will provide the basis for joint development of machine learning methods to develop strategies for fire prevention and firefighting in close cooperation with project partners from research, industry, and decision-makers.
As a member of the S.3 eScience group, you will work in a team of data scientists and benefit from the professional exchange on methods of machine learning, data management and applied statistics. In cooperation with other departments, methods from the field of data science are developed and software solutions implemented in Section S.3. We offer a wide range of possible applications and ideal conditions for creative minds.

We are looking for talented people to join us.

Your responsibilities include:

• You will actively promote the development of machine learning methods and statistical concepts for the analysis of data archives and experimental data on forest fire development and spread
• You will be responsible for collecting all relevant data for developing machine learning methods
• In cooperation with project partners from the natural and engineering sciences, you will identify relevant influencing factors for fire outbreaks and development and integrate them into your models
• Based on data describing e.g., climate, weather, land use and human activities, you will develop concepts for forest fire risk assessment and for context-sensitive early detection of critical conditions
• You develop high-resolution real-time models to predict fire and smoke development
• You support concept development, efficient fire monitoring and fighting
• In close cooperation with project partners, you derive recommendations for forest management and fire protection management based on your research results
• You communicate your research results at scientific conferences and in refereed journals

Your qualifications:

• Successfully completed scientific university studies in the field geoinformatics, geosciences, computer science or equivalent
• Good knowledge of data science with machine learning tools and data mining methods (e.g., Tensorflow, PyTorch, Pandas, Scikit-Learn)
• Good knowledge of at least one programming language (e.g., Python, Julia)
• Good knowledge of the visualization and evaluation of high-dimensional, complex data and interpretation of results, ideally with experience in the analysis of geodata
• Good knowledge and several years of professional experience in geoinformatics and software development are desirable
• Experience with version control systems (e.g., Git, Mercurial, Subversion) is desirable
• Experience in the processing and evaluation of satellite images and geodata (e.g., GIS) is an advantage
• Very good, precise, and addressee-oriented oral and written expression in English
• Good communication and information behaviour, initiative/commitment and ability to make decisions, ability to work in a team and willingness to cooperate, flexibility as well as conceptual, strategic and innovative thinking skills

We offer:

• Interdisciplinary research at the interface of politics, economics and society
• Work in national and international networks with universities, research institutes and industrial companies
• Outstanding facilities and infrastructure
• Flexible working hours and mobile working

Your application:

We welcome applications via the online application form by 06.04.2023. Alternatively, you can also send your application by post, quoting the reference number 63/23-S.3 to:

Bundesanstalt für Materialforschung und ‑prüfung
Referat Z.3 – Personal
Unter den Eichen 87
12205 Berlin
GERMANY
www.bam.de

Dr. Benner will be glad to answer any specific questions you may have. Please get in touch via the telephone number +49 30 8104‑3647 and/or by email to Philipp.Benner@bam.de.

BAM pursues the goal of professional equality between women and men. We therefore particularly welcome applications from women. In addition, BAM supports the integration of severely disabled persons and therefore especially welcomes their applications. With regard to the fulfilment of the job advertisement requirements, the application documents are examined individually. Recognised severely disabled persons will be given preferential consideration if they are equally suitable.

The advertised position requires a low level of physical aptitude.

BAM actively supports the compatibility of work and family and has been certified as a family- and life-phase-conscious employer by the "audit berufundfamilie" since 2015.