Internships and Master theses


Paid internship CaCTüS

CaCTüS is a fully funded 3-months internship at the Max-Planck-Institutes for Biological Cybernetics and Intelligent Systems in Tübingen and Stuttgart as well as the AI Center Tübingen, Germany. Participants will work on research projects spanning machine learning, electrical engineering, theoretical neuroscience, behavioral experiments and data analysis.

Who can apply?
Bachelor and master
students in the fields of computer science, maths, physics, engineering, neuroscience, psychology, cognitive science, bioinformatics and any other related fields.

Application deadline:

Dec 04, 2022


MPI Tübingen und Stuttgart


Computational Systems Neuroscience

The department of Computational Systems Neuroscience welcomes undergrad students from the fields of Biology, Neuroscience, Computer Science, Physics, or Engineering to participate in their research. They are offering projects in computational and experimental neuroscience, including modeling studies, studies in animal behavior and in neurophysiology.

Required skills: depending on project


University of Cologne


Lab head
Martin Nawrot

Visual and olfactory systems

The Department of Sensory and Sensorimotor Systems is open for students for internships and theses in their labs.

Research foci are visual and olfactory functions in the human and animal brain and their elicited behavioral responses, and other related topics in brain science. Research methods include visual psychophysics, zebrafish behavior and neuroscience, computational modeling, data analysis, human event related potential measurements, fMRI and eye tracking.

Eligibility: depending on requirements of individual training program

Required skills: depending on project


Max Planck Institute for Biological Cybernetics Tübingen


Head of the department
Li Zhaoping

Cognitive and neural mechanisms underlying learning and memory

The computational neuroscience group researches on the cognitive and neural mechanisms underlying learning and memory using computational methods. Topics for Bachelor/Master theses are available and the lab is open for collaborations, offering expertise on episodic memory, spatial navigation, hippocampus, neural networks, and reinforcement learning. The main language of communication is English.

Skills to be learned: reinforcement learning, neural networks, parallel computing

Required skills: programming


Ruhr University Bochum (RUB)


Group leader
Sen Cheng

High performance computing (HPC)

The Simulation and Data Lab Neuroscience offers students an insight into the world of High-Performance Computing (HPC) and how HPC can be efficiently used for neuroscience research, from modelling and simulation to data analysis and neuroimaging. Visiting students should have some programming skills, e.g. in C, C++ or Python, to be able to use their visit for a (first) project with HPC and/or data resources.

Skills to be learned: high-performance computing, data analysis, visualisation technology for neuroscience

Required skills: programming


Jülich Supercomputing Centre


Scientific coordination
Maren Frings

Master theses

Directional information in hippocampal place cells populations

Hippocampal place cells not only decode the current position but also the running direction of an animal. The interconnectedness between those codes is less explored but of fundamental importance for the understanding of hippocampal function in navigation and memory processes. This project will apply and refine analysis tools for extracellularly recorded spiking data obtained during free foraging. Students will be exposed to classic methods and literature on hippocampal in-vivo physiology and state-of-the art machine learning methods.

Eligibility: Student must be enrolled at University of Freiburg

Required skills: Experience in Python and statistical methods

Background: Neurobiology


Bernstein Center Freiburg, University Freiburg


Bernstein Coordination Site
Janina Radny

Modeling in neurophysiology and optogenetics

Depending on background and interest: Modeling single neurons and neuronal networks in the thalamocortical system (simple and biophysical models); analysis of single neuron and network encoding of sensory/behavioral stimuli and interactions between neurons (data from large-scale silicon probe in vivo experiments with targeted optogenetic interventions); information theoretic analysis of neural data; identification of neuronal ensembles.

Student background: physics, applied mathematics, computer science, data science

Required skills: depending on project: linear algebra, differential equations, data visualization, Python


University of Heidelberg, Institute of Physiology and Pathophysiology


Bernstein Coordination Site
Janina Radny