PhD position - Bridging classical and AI- approaches analysis of paralle

Organisation/Company Forschungszentrum Jülich Research Field All Researcher Profile First Stage Researcher (R1) Final date to receive applications 19 Jan 2038 - 03:14 (UTC) Country Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

IAS-6 - Theoretical Neuroscience

Area of research:

Your Job:

This PhD project bridges between classical analytical methods and modern AI-based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods are developed that prioritize interpretability and reduce data dependency by imposing desirable constraints on model behavior. We will divide our work into three thrusts:

• Thrust A: augment classical spike train analysis methods, particularly those developed by Prof. Grün and others for detecting synchronous spiking activity, with AI-based enhancements. After profiling the classical methods’ bottlenecks, we will replace or supplement them with ML-based surrogates, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost.
  
  This hybridization aims to accelerate performance while maintaining, if not improving, analytical rigor. The improved modules will be integrated into an updated analytical pipeline and validated against benchmark datasets from prior studies.
• Thrust B: make contemporary ML-based techniques more interpretable and biologically meaningful in their application to neural population coding. We will build upon recent advances in graph neural networks (GNNs), particularly those described by which offer a promising architecture for modelling population-level neural interactions. Prior work has emphasized rate-based codes due to their relative simplicity; our approach will extend these models to capture temporal structure within spike trains, moving towards analyses sensitive to firing rates and precise timing relationships.
  
  To keep these advanced models from becoming opaque “black boxes,” we will integrate post-hoc explainability tools such as SHAP values.
• Thrust C: evaluate all developed methods rigorously using synthetic and real-world datasets. Synthetic benchmarks will be generated using established generative models capable of producing ground-truth synchronous patterns under varying conditions to enable systematic validation. All software arising from this work, including improved analysis pipelines and benchmarking datasets, will be released through an open-source library.

Your Profile:

• A Master’s degree with a strong academic background in physics, mathematics, computer science, or a related field.
• Proficiency in at least one programming language (Python, C++, …).
• Experience in neuroscience is an advantage.
• Good analytical skills with a sound understanding of data evaluation.
• Good organisational skills and ability to work systematically, independently, and collaboratively.
• Effective communication skills and an interest in contributing to a highly international and interdisciplinary team.
• Motivation for academic development, supported by bachelor’s and master’s transcripts and two reference letters.
• Working proficiency in English for daily communication and professional contexts (TOEFL or equivalent or exemption required).
• Knowledge of German is beneficial.

Our

Offer:

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! This HDS-LEE PhD position will be located at Forschungszentrum Jülich and RWTH Aachen University. We offer ideal conditions for you to complete your doctoral degree:

• World-class science environment at the interface between neuroscience and digital technologies, enabling scientific progress on the most complex known systems.
• Outstanding scientific and technical infrastructure.
• A highly motivated group and an international and interdisciplinary working environment at one of Europe’s largest…