Graduate School
The in-depth research-oriented training after the master's degree is a central activity area of SECAI. Due to the special, interdisciplinary orientation of the school, there are not only doctoral students but also so-called clinician scientists, who have already completed a doctoral thesis and whose goal in SECAI is a (first) major publication.
Each year SECAI hires a cohort of about ten doctoral students and clinician scientists, typically starting in September. Calls for the positions of the Graduate School are usually published in spring of the corresponding year. You can find them on our job portal when they are open. The school favors a “tight” three-year model for completing doctoral theses, but also understands that this plan may need adjustment due to interdisciplinary differences, e.g., clinician scientists finishing in two years, personal circumstances and professional causes.
In addition to the doctoral students and clinician scientists directly funded by SECAI, other AI researchers of comparable career stages will be integrated into the School. These associate members will be closely integrated scientifically and organizationally and will have access to all SECAI funding programs. In addition, SECAI networks with other thematically relevant graduate programs and implements joint activities.
Doctoral Students and Clinical Scientists
Topic: Intuitive Modelling support for Knowledge-Based Systems
Topic: Explaining Composite AI: From Formal Analysis to Overall Understanding
Topic: Sim2Real: Simulated Training and Test Data for Biomedical Image Analysis
Topic: Spinnaker Cloud Computing for Computer-aided Drug Discovery
Topic: Neuromorphic Information Processing: Hardware Realization of the Modified Stochastic Synaptic Model
Topic: Surg-Morph: Neuromorphic Surgical Video Processing on the SpiNNaker2 Platform
Topic: Spinnaker Cloud Computing for Computer-aided Drug Discovery
Topic: MemAI: Memristive Dynamic Models for Smart AI Systems
Topic: 2D-Material-Based Synapses for Neuromorphic Computing
Topic: AI in Oncology
Topic: AI Controlled Wireless Mesh Networks for Medical Applications in Crisis Situations (AWESOME)
Topic: Personalization in oncology – Prediction of cancer therapy effectiveness from Omics data through structural modeling and AI
Topic: Teaching artificial intelligence conformational dynamics of proteins
Topic: GeoSOP: Geometry for surgical outcome prediction
Topic: The Use of AI by the Civil Service in Light of the Principles of Democracy and the Rule of Law
Topic: SuperTouch - Data-driven tactile sensing for robot-assisted cancer surgery
Topic: Adaptive / Context Sensitive Data Provision in Mobile Applications
Topic: Features of Fitness Landscapes
Topic: Explainable AI for reliable computer assisted surgery
Topic: External Systems Biology Knowledge Integration in Large Language Models
Topic: What are GPCR’s and why are they important?
Topic: Large Language Models (LLMs) and the Formalization of Mathematics
Topic: Theoretical Foundations of Ethical and Interpretable Diffusion Models
Topic: Multi-agent Belief Management
Topic: StochasticAI – Integrating Stochastic Modeling with AI
Topic: FunLog: A Functional Approach to Modular Logic Programming