34 PhD positions at ScaDS.AI Dresden/Leipzig, Germany - apply by April 09, 2025 (CEST)

[Frank Loebe]

Dear all,                 [thanks, if you share this with interested parties]

ScaDS.AI Dresden/Leipzig [1] currently offers 34 open positions for Research Associates / PhD Students (f/m/x) within its Graduate School, together with topics, mentors and host institutions.

Links:   Topic descriptions [2],    Formal job posting [3]

Application deadline:   Wed, April 09, 2025   (23:59 CEST [4])
Positions' start dates:   between July and December 2025
Work location:               Germany, Dresden or Leipzig (topic-specific)

Topics belong to these areas:
▶️ Knowledge Representation and Inference
▶️ Mathematical Foundations of AI and Representation Learning
▶️ Scalable ML and LLM Inference
▶️ Time Series Analysis and Reinforcement Learning
▶️ Visualization and Causal Inference
▶️ ML and Ethics for Protein Design and Chemical Reactions

Selected further info follows below. We look forward to all applications!

Best regards,
Frank Loebe





Selected facts about the offered positions:

• competitive payment, topic-specific (see descriptions [2])
  
• limited to 3 years
• aim at obtaining further academic qualification (usually PhD)
• excellent hardware facilities available for high performance computing and scalable AI
  
  

Candidate profile should fulfill:

• outstanding university degree (typically M. Sc.) in Computer Science, Data Science, Statistics, Mathematics or another relevant field study with good GPA
• very good programming skills and AI/ML knowledge
• good written and spoken English skills (CEFR level C1 or higher)
  
  

Full list of topics, descriptions at [2]:

• Knowledge Representation and Inference
  
• T1 Linear Time Algorithms for Ontology-Mediated Querying
• T2 Combining Description Logics with Argumentation Frameworks for Repair
  
  
• Mathematical Foundations of AI and Representation Learning
  
• T3.1 Manifold Learning over Dynamic Point Clouds
• T3.2 Information Theory for Point Cloud Data
• T4.1 Representation Learning for Multimodal Single-Cell Data
• T4.2 Integration of Structured Knowledge into Language Models for Cell Biology
• T4.3 Synthetic Data Generation and Integration for Enhanced Single-Cell Analysis
• T4.4 Self-Supervised Feature Extractors for Multi-Modal Biological Data
• T5.1 Physics Foundations: Representation Learning in Large AI Models
• T5.2 Math Foundations: Geometric Manifold Learning
• T5.3 Computational Foundations: Interpretable Latent Dynamic Discovery
• T5.4 Scientific Application: Latent Geometry of Information Processing in Complex Dynamical Systems
  
  
• Scalable ML and LLM Inference
  
• T6.1 Reconfigurable Computing Architectures for Large Language Models
• T6.2 Compilers for LLMs on Data-Centric Architectures
• T6.3 Distributed Learning of Large AI Models on Hardware Accelerators
• T6.4 Efficient Inference of Large AI Models on Specialized Hardware Architectures
• T7.1 AI-Driven Quantum Chemistry for Accelerated Drug Discovery
• T7.2 Accelerating Drug Discovery with Ultra-Large Library screening on SpiNNaker2
• T7.3 Acceleration of Electron-Density Approximation for Drug Discovery on the Massively-Parallel SpiNNaker2 Platform
• T8 Transfer Learning for Multi-Objective Neural Architecture Search
  
  
• Time Series Analysis and Reinforcement Learning
  
• T9.1 Time Series Methods for High-Frequency Distributed Acoustic Sensing Glacier Data
• T9.2 Biologically Informed AI Methods for Time Series Data
• T9.3 Weakly supervised detection and attribution of climate and terrain-induced extremes in photosynthetic activity
• T10.1 Transparency of Computationally Rational User Models
• T10.2 Explainable Reinforcement Learning in Robotics and Fluidics
  
  
• Visualization and Causal Inference
• T11.1 Design, Implementation, and Evaluation of a Framework for AI-Driven Data Storytelling for Scientific Visualizations
• T11.2 Verbalization of Scientific Data: Developing Flexible Generative Text Approaches for Scientific Visualization
• T12 Fertilization, Climate and Biodiversity: Visualisation, Modelling, Causal Inference
  
  
• ML and Ethics for Protein Design and Chemical Reactions
• T13.1 New Protein Design Technologies – From Foundation Models to Specialized Models
• T13.2 Responsible Usage of Biodesign Tools – Uncertainty Measures and Epistemological Investigations
• T14.1 Coarse-Grained Model for Chemical Reaction Mechanism
• T14.2 Physics-Based Generative Model for Chemical Reaction Mechanisms
• T15.1, T15.2 Deep Learning of Protein-Ligand Interaction Fingerprints Based on Functional Atom Matching for Applications in Drug Discovery and Protein Design

Diversity, Equity, Inclusion:
The University strives to employ more women in academia and research. We therefore expressly encourage women to apply. The University is a certified family-friendly university. We welcome applications from candidates with disabilities. If multiple candidates prove to be equally qualified, those with disabilities or with equivalent status pursuant to the German Social Code IX (SGB IX) will receive priority for employment.



Links/References

[1] ScaDS.AI Dresden/Leipzig, one of 6 German Competence Centers on AI
https://scads.ai/

[2] topic descriptions
https://scads.ai/positions2025

[3] formal job/vacancy posting at TU Dresden
https://www.verw.tu-dresden.de/StellAus/stelle.asp?id=12032&lang=en

[4] page with conversions of Apr 09, 23:59 CEST to other regional times
https://www.timeanddate.com/worldclock/fixedtime.html?iso=20250409T2159
Note that timezone CEST is derived from the formally valid statement on [3]:
"April 09, 2025 (stamped arrival date of the university central mail service or the time stamp on the email server of TUD applies)"


--
Frank Loebe
Coordinator
ScaDS.AI Graduate School

Leipzig University
ScaDS.AI Dresden/Leipzig - Center for
Scalable Data Analytics and Artificial Intelligence
Humboldtstrasse 25, 04105 Leipzig, Germany