Background: The human brain consists of approximately 86 × 10⁹ neurons, whereas the brain of larval zebrafish contains only about 10⁵. Despite its smaller size, the fundamental architecture of vertebrate brain circuits is evolutionarily conserved, making zebrafish a powerful model organism for neuroscience. In addition, larval zebrafish are small and transparent, thereby offering a unique opportunity for in vivo imaging of whole-brain neuronal activity. Indeed, the zebrafish was the first vertebrate organism in which neuronal activity throughout the whole brain was recorded in real time (Ahrens et al., 2013). Although we can now literally see the fish thinking, the obtained imaging data sets are large and require computational methods to gain fundamental insights into the activity patterns of neural networks and their interactions (Haesemeyer et al., 2019).
In previous work, we established a custom-built microfluidic chip ("NeuroExaminer") made entirely of glass that enables pharmacological stimulation and whole-brain calcium imaging of larval zebrafish using light-sheet microscopy (Schrödter et al., 2024). To analyze the vast amounts of imaging data produced by these recordings (about 100 GB per brain), we developed a robust computational framework in Python for motion correction, neuron segmentation, atlas registration, and activity quantification.
Project: We are now looking for an enthusiastic and highly motivated PhD student to extend and apply this framework in a new project that investigates the neural effects of Ritalin (methylphenidate), a commonly prescribed psychostimulant for attention deficit hyperactivity disorder (ADHD) and a widely abused drug for cognitive performance enhancement. The project will use whole-brain calcium imaging of transgenic zebrafish larvae to monitor neuronal responses to Ritalin exposure over time. The aim is to characterize spatiotemporal activity changes across the brain and identify specific neuronal populations and regions affected by Ritalin, with a particular focus on brain regions involved in attention, reward processing, and motor control – key targets in the treatment of ADHD.
Building on the already established Python-based computational framework, you will perform large-scale data analysis of light-sheet imaging datasets. This includes preprocessing, motion correction, segmentation of tens of thousands of neurons, registration to reference brain atlases, and statistical comparison between control and treatment groups. You will further develop and improve the computational tools for time-series analysis, activity classification, and visualization of functional brain maps. Additional extensions may include incorporating deep learning techniques and parallelization using GPUs to accelerate data processing and improve scalability.
This interdisciplinary project combines experimental neurobiology with advanced computational data analysis and contributes to a better understanding of how psychostimulants modulate neural circuits at the whole-brain level. Ultimately, this research may lead to the development of more effective treatments for neuropsychiatric disorders such as ADHD.
Your profile: You should have strong programming skills (particularly in Python) and enjoy working with large datasets. Ideally, you already have experience in image analysis, machine learning, or neural data processing. Familiarity with tools such as NumPy, Pandas, PyTorch/TensorFlow, and/or ANTs is a plus, but not required. Prior knowledge in biology or microscopy is not necessary, but a keen interest in neuroscience and data-driven exploration of brain function is essential. You will join an international and interdisciplinary team, so good communication skills in English are expected.
Position details: The PhD position is offered in the Köster Lab at the Zoological Institute (https://www.tu-braunschweig.de/en/zoology) in Braunschweig, Germany. The preferred starting date is the 01.01.2026, and the duration is 3 years. The position is part-time suitable, but should be occupied 100% and is aimed to lead to a PhD degree as Dr. rer. nat. at the Life Sciences Faculty of the Technical University (TU) of Braunschweig.
The payment is made according to task assignment and fulfillment of personal requirements to salary group EG 13 TV-L, 50% (approximately: 1.750 €/month net). Applicants from non-EU countries may have to successfully complete a visa process before hiring can take place and are welcomed to apply.
The TU Braunschweig aims to increase the share of women in academic positions. Applications from female candidates are very welcome. Where candidates have equal qualifications, preference will be given to female applicants. Candidates with handicaps will be preferred if equally qualified. Please enclose a proof when applying for the position.
Applications: should be sent by e-mail to Reinhard Köster (r.koester@tu-bs.de), and must contain the following documents.
All documents should be in the PDF format; preferably, please provide the entire application in a single file. Personal data and documents relating to the application process will be stored electronically. Please note that application costs cannot be refunded. Deadline for applications: until position is filled.
For any questions, please contact: Dr. Jakob von Trotha or Prof. Reinhard Köster
Email: j.von-trotha(at)tu-braunschweig.de, r.koester(at)tu-bs.de
Further reading:
Ahrens, M.B., Orger, M.B., Robson, D.N., Li, J.M., & Keller, P.J. (2013) Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat Meth, 10, 413–420.
Haesemeyer, M., Schier, A.F., & Engert, F. (2019) Convergent Temperature Representations in Artificial and Biological Neural Networks. Neuron, 103, 1123–1134.e1126.
Schrödter, D., Mozafari, M., Fichtner, J., v. Trotha, J., Köster, R: W., Dietzel, A. (2024). A 3D Tailored Monolithic Glass Chip for Stimulating and Recording Zebrafish Neuronal Activity with a Commercial Light Sheet Microscope. Front. Lab Chip Technol. 3, 1346439
