Research Associates

As part of the German government’s artificial intelligence (AI) strategy, the successful Saxon competence center ScaDS.AI Dresden/Leipzig (Center for Scalable Data Analytics and Artificial Intelligence) is being expanded into a leading German competence center for Big Data and AI. For this purpose, the following positions at the Dresden site are available at ScaDS.AI as soon as possible as


Research Associate
(Subject to personal qualification employees are
remunerated according to salary group E 13 TV-L)


starting at the next possible date. The positions are limited and the period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz-WissZeitVG).

Research Associate (Position: ScaDS-22.1)


Professional assignment:

Chair of Applied Stochastics (Prof. Dr. rer. nat. habil. Anita Behme) and Chair of Probability Theory (Prof. Dr. rer. nat. habil. René Schilling)

Research area:

applications of AI in stochastic processes, e.g. statistics of stochastic processes

Tasks:

research on the statistical analysis of (clusters) of extreme values of a stochastic process in continuous time; presentation and publication of research results in English; administrative support in running the project, e.g. organization of workshops, reporting. There will be the possibility to offer lectures in one’s own research field.

Requirements:

very good university degree in mathematics and PhD in mathematics or computer science; strong scientific background in at least one of the following areas: theory of AI, applications of AI in the sciences, statistics of stochastic processes, theory of stochastic processes; ability and willingness for interdisciplinary cooperation; very good command of written and spoken English.

Duration:

3 years

More Information and Application:
https://tu-dresden.de/vacancy/9601

Research Associate (Position: ScaDS-22.2)


Professional assignment:

Chair of Computational Landscape Ecology (Prof. Dr. rer. nat. Anna Cord)

Research area:

Biodiversity and ecosystem monitoring

Tasks:

research on automated species identification and trait recognition from images, video and audio recordings; design, implementation and analysis of machine learning algorithms using available field data on biodiversity and ecosystem functioning (e.g. bird, bat and insect sounds, plant traits and vegetation phenology, microclimate); development of methods for analyzing biotic interactions and ecological networks; active participation and collaboration in the ScaDS.AI consortium; presentation of research results at workshops and international conferences; publication of research results in peer-reviewed English-language journals.

Requirements:

very good university degree in data/computer science, machine learning, mathematics or a comparable engineering or natural science (combined with a strong interest in environmental/ecological applications) or in quantitative ecology (with a strong interest in computational and machine learning techniques); curiosity and strong interest in working in an interdisciplinary and international team; self-reliant, flexible and result-oriented work ethic; very good command of written and spoken English; prior knowledge in spatial data analysis and modeling is an advantage.

Duration:

3 years

More Information and Application:
https://tu-dresden.de/vacancy/9602

Research Associate (Position: ScaDS-22.3)


Professional assignment:

Chair of Stochastic Calculus and Financial Mathematics (Prof. Dr.rer. nat. Martin Keller-Ressel)

Research area:

Geometric Representation Learning

Tasks:

research on geometric methods and representations in machine learning, in particular with focus on hyperbolic geometry, embedding methods for graphs and networks, supervised learning in non-Euclidean settings, embedding and learning for data under hierarchical and relational constraints, theoretical guarantees and error bounds for geometric embedding methods, prediction and inference of contagion and diffusion processes in non-Euclidean geometry.

Requirements:

very good university degree in mathematics or in computer science and closely related fields with strong theoretical/methodological focus; curiosity and strong interest in rigorous, methodical fundamental research; very good programming skills, preferable in Python; very good command of written and spoken English. Prior knowledge in linear algebra, mathematical geometry, stochastics and optimization is preferable.

Duration:

3 years

More Information and Application:
https://tu-dresden.de/vacancy/9603

Research Associate (Position: ScaDS-22.4)


Professional assignment:

Chair of Big Data Analytics in Transportation (Prof. Dr. rer. pol. Pascal Kerschke)

Research area:

AI and ML Algorithms / Methods (Federated and Efficient Learning)

Tasks:

examination of problems from the domains of machine learning and black-box optimization. Development of feature-based algorithms, integration into established benchmarking tools, and empirical analysis of their suitability and effectiveness; presentation and publication of research results in English.

Requirements:

very good university degree in computer science, data science, machine learning, computational statistics or a related field; prior knowledge in the fundamentals and application of machine learning and/or a domain of optimization (e.g., continuous optimization, evolutionary computation); curiosity and strong interest in fundamental research; very good programming skills in R or python; very good command of written and spoken English.

Duration:

3 years

More Information and Application:
https://tu-dresden.de/vacancy/9604

Research Associate (Position: ScaDS-22.5)

Professional assignment:

Chair of Computational Logic (Prof. Dr. rer. nat. Sebastian Rudolph)

Research area:

Knowledge Representation and Engineering

Tasks:

foundational research on formal properties (e.g., expressivity, decidability, computational complexity) of logic-based knowledge representation formalisms such as description logics, existential rule languages, modal logics, fragments of first-order logics, etc.; publication and presentation of results at top international conferences in English.

Requirements:

excellent university degree in mathematics or computer science; prior knowledge and publications related to formal logic, model theory, computability and/or complexity theory. Experience and Interest in knowledge representation and automated reasoning; very good command of written and spoken English.

Duration:

until Dec. 31st 2023

More Information and Application:
https://tu-dresden.de/vacancy/9605


Research Associate (Position: ScaDS-22.6)


Professional assignment:

Chair of Processor Design (Prof. Dr. Akash Kumar)

Research area:

Architecture, Scalability and Security

Tasks:

design and develop a framework for implementing high-performance and energy-efficient accelerators for machine learning models. The intended framework will exploit the inherent errortolerance of machine learning algorithms to introduce deliberate approximations (inaccuracies) at the various layers of the computation stack to achieve energy-efficient accelerators without any significant loss in the output accuracy of the implementation. To explore the large design space enabled by these
approximation knobs, identify and adapt a multi-objective optimization technique such as Bayesian optimization and genetic algorithms. Presentation and publication of the research results in English.

Requirements:

very good Master’s degree in mathematics, computer science, or a comparable engineering or natural science; very good programming skills in Python, C/C++; good understanding of machine learning, in particular, artificial neural networks; good programming skills (especially on scripting, assembly-level and C languages) as well as good hardware-design skills (especially using VHDL/Verilog and component-based design); experience with high-level synthesis and multi-objective optimization techniques; very good command of written and spoken English.

Duration:

3 years

More Information and Application:
https://tu-dresden.de/vacancy/9606

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