10 positions – Research Assistant – salary grade E 13 TV-L Berliner Hochschulen – 1st qualification period (PhD candidate)

Technische Universität Berlin offers an open position:

10 positions – Research Assistant – salary grade E 13 TV-L Berliner Hochschulen – 1st qualification period (PhD candidate)

part-time employment may be possible

The positions are part of the Graduate School of the 'Berlin Institute for the Foundations of Learning and Data' (BIFOLD). BIFOLD conducts scalable agile fundamental research in the field of AI, primarily in the areas of data management (DM) and machine learning (ML) in the German AI metropolis of Berlin. The institute is part of the network of six national competence centres for artificial intelligence research in Germany. The joint task of the centres is to establish Germany as a top location for AI technologies.

PhD students in the BIFOLD Graduate School benefit from comprehensive guidance provided by renowned international scientists and interdisciplinary exchange at one of the world's leading AI research centres. As part of the Graduate School, we offer scientific training at the highest level, a variety of training activities, such as summer schools and workshops, mentoring, as well as internal and external networking opportunities (e.g., access to the international BIFOLD network, funding for conference visits, guest scientist programs). BIFOLD stands for an international, collegial and family-friendly working environment.

Faculty IV – Berlin Institute for the Foundations of Learning and Data (BIFOLD)
Reference number: IV-835/23 (starting at 01/10/2024 / for 4 years / closing date for applications 26/01/2024)

Working field:

Based on the overarching research foci of BIFOLD, the Graduate School offers PhD projects in the areas of current challenges in AI, Data Science and distributed analysis of large amounts of data, with a focus on DM, ML, and their intersection; including the development of novel theories, algorithms, and technologies, as well as prototypical systems and tools.

We are looking to fill five positions within the DM focus area, led by Professor Markl, and five with a focus on ML, led by Professor Müller. You can find an overview of our research groups at https://www.bifold.berlin/​research/​workgroups. Brief descriptions of the current research projects of the individual BIFOLD research groups and the associated doctoral subject areas can be found at https://​www.bifold.berlin/​education/​thesis-opportunities.

Requirements:

• Successfully completed academic university degree (Master, Diploma, or the equivalent) in computer science (e.g., theoretical, methodological-practical, or technical computer science) or closely related fields of study with a focus on at least one BIFOLD core area,
• good programming skills (e.g., Python, Java, Scala, C/C++, Rust),
• for positions in the field of DM: hands-on experience in the use and (optionally) implementation of big data systems (e.g., Apache Flink, Apache Spark, Dask) or database systems (e.g., PostgreSQL),
• for positions in the area of ML: knowledge of machine learning theories and methods (e.g., core methods, deep neural networks), practical experience in developing and applying ML algorithms, experience with linear algebra/​neural network frameworks (e.g., NumPy, PyTorch, TensorFlow, JAX),
• for positions in the intersection of DM/ML: hands-on experience in applied ML (feature and model selection, ML frameworks, model evaluation and debugging), as well as (optional but advantageous) experience in multi-modal data representations, alignment, data-centric ML pipelines, and ML for applications such as healthcare or remote sensing,
• the ability to teach in German and/or in English is required; willingness to acquire the respective missing language skills,
• early experience in research and paper writing are an advantage,
• experience in teaching and didactic competence are an advantage.

We are looking for highly motivated, curious, enthusiastic, and results-oriented researchers with excellent academic records and strong research interest in the areas of DM, ML and their intersection.

Please send your application, quoting the job reference number and including the usual documents (in particular, filled-in application form https://​www.bifold.berlin/​education/​graduate-school/​admission, letter of motivation, latest CV, copies of your Bachelor's and Master's certificates, official copies of your academic transcripts, list of publications, and names and contact details of at least 2 referees whose letters should be available by the deadline of this call), preferably in English, by email as one file in pdf format to Prof. Dr. Volker Markl and Prof. Dr. Klaus-Robert Müller, at gsapplication@bifold.tu-berlin.de.

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage:
https://www.tu.berlin/abt2-t/​services/​rechtliches/​datenschutzerklaerung-bei-bewerbungen

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities.