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Startseite Schütte, Christof, Prof. Dr.

Schütte, Christof, Prof. Dr.

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Position
Präsident
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4026
Telefon
+49 30 84185-101
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E-Mail
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Personal Homepage
Bereich
Mathematics of Complex Systems

Projekte

Projekte als Leitung

Signals in Neural network

Modeling Synaptic Plasticity: A Data-Driven Approach to Neurotransmission Dynamics

This project investigates the role of functional proteins in presynaptic neurotransmission and synaptic plasticity by combining data-driven and theory-driven modeling...

Modeling Synaptic Plasticity: A Data-Driven Approach to Neurotransmission Dynamics
Model-regularized Learning of Complex Dynamical Behavior

Model-Regularized Learning Of Complex Dynamical Behavior

This project is planned to couple machine learning approaches, especially from the field of Deep Learning, with (reduced) ODE models in the sense that the model becomes...

Model-Regularized Learning Of Complex Dynamical Behavior
opinion dynamics

Opinion Dynamics

The literature contains a manifold of fundamental models for the dynamical changes of opinions through social influence during social interaction (personal, social...

Opinion Dynamics
Pareto-ML-Optimization-Cycle

AA1-19 Drug Candidates as Pareto Optima in Chemical Space

The search for novel drug candidates that, at the same time, act with high efficacy, comply with defined chemical properties, and also show low off-target effects can be...

AA1-19 Drug Candidates as Pareto Optima in Chemical Space

Projekte als Mitglied

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Modeling and Simulation of Innovation Spreading in Ancient Societies

A central challenge in the study of historical civilizations is the construction of evidence-based narratives describing complex processes of change that affected and...

Modeling and Simulation of Innovation Spreading in Ancient Societies
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High-throughput Analysis of Large Medical Data

Data is growing and moving faster than healthcare organizations can consume it; 80% of medical data is unstructured and is clinically relevant. Getting access to this...

High-throughput Analysis of Large Medical Data
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Uncertainty Quantification for Bayesian Inverse Problems with Applications to Systems Biology

This research is carried out in the framework of MATHEON supported by Einstein Foundation Berlin.

Uncertainty Quantification for Bayesian Inverse Problems with Applications to Systems Biology
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Sparse Compressed Sensing Based Classifiers for -Omics Mass-Data

Tumor diseases rank among the most frequent causes of death in Western countries coinciding with an incomplete understanding of the underlying pathogenic mechanisms and a...

Sparse Compressed Sensing Based Classifiers for -Omics Mass-Data